A review on face recognition systems: recent approaches and challenges

  • Published: 30 July 2020
  • Volume 79 , pages 27891–27922, ( 2020 )

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  • Muhtahir O. Oloyede 1 , 2 ,
  • Gerhard P. Hancke 2 &
  • Hermanus C. Myburgh 2  

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Face recognition is an efficient technique and one of the most preferred biometric modalities for the identification and verification of individuals as compared to voice, fingerprint, iris, retina eye scan, gait, ear and hand geometry. This has over the years necessitated researchers in both the academia and industry to come up with several face recognition techniques making it one of the most studied research area in computer vision. A major reason why it remains a fast-growing research lies in its application in unconstrained environments, where most existing techniques do not perform optimally. Such conditions include pose, illumination, ageing, occlusion, expression, plastic surgery and low resolution. In this paper, a critical review on the different issues of face recognition systems are presented, and different approaches to solving these issues are analyzed by presenting existing techniques that have been proposed in the literature. Furthermore, the major and challenging face datasets that consist of the different facial constraints which depict real-life scenarios are also discussed stating the shortcomings associated with them. Also, recognition performance on the different datasets by researchers are also reported. The paper is concluded, and directions for future works are highlighted.

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This work was supported by the Council for Scientific and Industrial Research (CSIR), South Africa.

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Oloyede, M.O., Hancke, G.P. & Myburgh, H.C. A review on face recognition systems: recent approaches and challenges. Multimed Tools Appl 79 , 27891–27922 (2020). https://doi.org/10.1007/s11042-020-09261-2

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Received : 08 August 2019

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Published : 30 July 2020

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DOI : https://doi.org/10.1007/s11042-020-09261-2

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ORIGINAL RESEARCH article

Research on face recognition and privacy in china—based on social cognition and cultural psychology.

\r\nTao Liu*

  • Department of Sociology, Hangzhou Dianzi University, Hangzhou, China

With the development of big data technology, the privacy concerns of face recognition have become the most critical social issue in the era of information sharing. Based on the perceived ease of use, perceived usefulness, social cognition, and cross-cultural aspects, this study analyses the privacy of face recognition and influencing factors. The study collected 518 questionnaires through the Internet, SPSS 25.0 was used to analyze the questionnaire data as well as evaluate the reliability of the data, and Cronbach’s alpha (α coefficient) was used to measure the data in this study. Our findings demonstrate that when users perceive the risk of their private information being disclosed through face recognition, they have greater privacy concerns. However, most users will still choose to provide personal information in exchange for the services and applications they need. Trust in technology and platforms can reduce users’ intention to put up guards against them. Users believe that face recognition platforms can create secure conditions for the use of face recognition technology, thus exhibiting a higher tendency to use such technology. Although perceived ease of use has no significant positive impact on the actual use of face recognition due to other external factors, such as accuracy and technology maturity, perceived usefulness still has a significant positive impact on the actual use of face recognition. These results enrich the literature on the application behavior of face recognition and play an important role in making better use of face recognition by social individuals, which not only facilitates their daily life but also does not disclose personal privacy information.

Introduction

Face recognition is a biometric recognition technology that uses pattern matching to recognize individual identities based on facial feature data. Compared to traditional non-biological recognition and physiological feature recognition technology, face recognition technology has specific technical advantage ( Jiang, 2019 ). Nowadays, relying on ubiquitous mobile camera devices, face recognition technology has been widely used in various fields, including face attendance, face payment, smart campus, access control system, and security system, which demonstrate the advances in the face recognition service level in the intelligent hardware system. Face recognition technology has dramatically improved the intelligence level of business systems in these fields. The human face is rich in features. In the society of acquaintances in the past, the face was the foundation for us to involve emotional communication and social relations with others.

Technology has been one of the most important factors that changed the way of life and commercial activities of human society. With continuous innovation and the development of technology, human society is changing rapidly. Technological innovation has changed people’s lifestyles in the spheres of shopping, education, medical services, business organizations, and so on. “Technology is not only an essential tool for finding out new ways to join different actors in service innovation processes, but also as an element able to foster the emergence of new and ongoing innovations” ( Ciasullo et al., 2017 ). For example, in the healthcare service ecosystem, health care providers adapt to the innovative medical service ecosystem so that patients can obtain better medical services. Medical service innovation has had a great impact on the continuous reconstruction of the service ecosystem ( Ciasullo et al., 2017 ). Technology forces the market to change constantly, and the changing market leads business organizations to innovate. “The contemporary world is characterized by a fast changing environment. Business organizations are faced with the challenge of keeping pace with developments in the field of technology, markets, cultural and socio-economic structures” ( Kaur et al., 2019 ). In the era of big data and information, business organizations must “to explore how cognitive computing technology can act as potential enabler of knowledge integration-based collaborations with global strategic partnerships as a special case” ( Kaur et al., 2019 ).

At present, innovations in network technology provide the greatest convenience and advantages for organizations dealing with such networks. “Small and medium-sized enterprises (SMEs) have been considered the most innovative oriented businesses in developed countries even in emerging markets acting as pioneer in the digital transformational word.” Meanwhile, it is important for technology upgrading, knowledge spillover, and technology transfer to explore SMEs’ competitiveness ( Del Giudice et al., 2019 ).

Knowledge and technology transfer is a “pathway” for accelerating economic system growth and advancement. Technology transfer can be explored from theory to practice for knowledge and technology. From the users’ perspective, technology transfer affects their sense of use and experience ( Elias et al., 2017 ). Big data analytics capabilities (BDAC) represent critical tools for business competitiveness in highly dynamic markets. BDAC has both direct and indirect positive effects on business model innovation (BMI), and they influence strategic company logics and objectives ( Ciampi et al., 2021 ). “In the world of Big Data, innovation, technology transfer, collaborative approaches, and the contribution of human resources have a direct impact on a company’s economic performance.” Therefore, big data companies should make corresponding changes in management and strategy. Moreover, skilled human resources have a positive contribution to the company’s economic performance. “Information and knowledge are the foundation on which act for aligning company’s strategies to market expectations and needs” ( Caputo et al., 2020 ).

With the arrival of the era of artificial intelligence, intelligent social life has become a reality, and artificial intelligence has become a new engine for China’s economic and social development. According to the latest data released by the China Internet Information Center, the number of artificial intelligence enterprises in China ranks second in the world ( CNNIC, 2020 ). As a new technology, face recognition—a typical application of artificial intelligence—rises with the construction of a smart city According to the statistics presented in the Report on In-depth Market Research and Future Development Trend of China’s Face Recognition Industry (2018–2024) released by the Intelligence Research Group, it is estimated that the face recognition industry in China will reach 5.316 billion Yuan by 2021 ( Biometrics Identity Standardization [BIS], 2020 ). As the gateway connecting humans and intelligence face recognition has excellent development potential.

Given that the modern era emphasizes looks, the face remains socially functional, but technology has given it new meaning and a mission. The attributes and features of a facial image are enough to convey a person’s identity. When our face is tied to our personal information and even used as a password substitute, it is no longer the traditional concept of face. Face recognition technology can extract personally identifiable information, such as age, gender, and race, from images. To some extent, in the Internet age, almost everyone’s personal information is displayed without any protection.

With the technical support of big data, user portraits based on facial recognition and a variety of personal data have increasingly become identification for individuals in this day and age ( Guo, 2020 ). From face-swapping apps, access by face recognition to Hangzhou Safari Park, the application of face recognition in subway security checks, to the formulation of the Personal Information Protection Law of the People’s Republic of China (PRC), a series of public opinions have brought face recognition to the forefront. On the other hand, Internet privacy, which has been neglected so far, is increasingly taken seriously by the public.

The issues of face recognition and privacy have been studied extensively by experts and scholars in their respective fields, but there are few empirical studies on the combination of the use of face recognition and personal privacy security. At present, most scholars’ research on face recognition focuses on face recognition algorithms, recognition systems, legal supervision and security, users’ willingness to accept face payments, and the application of face recognition in the library. No quantitative research has been conducted on the relationship between the use of face recognition technology and people’s attitudes toward privacy issues. Therefore, based on the two main determinants of the technology acceptance model (TAM) and according to public attitudes toward privacy and the specific context of the use of face recognition in the current networked environment, variables such as privacy concerns, risk perception, and trust are introduced in this study to build the hypothesis model of the actual use of face recognition. The concept of privacy concerns is applied to the research on personal information security behavior of facial recognition users, which further expands the practical scope of the privacy theory and provides suggestions to promote the development of facial recognition applications.

This research makes two contributions. First, it demonstrates the impact of privacy concerns, perceived risk, trust, social cognition, and cross-cultural aspects on facial recognition. This result enriches face recognition literature, and a hypothesis model based on perceived ease of use and perceived usefulness—the two determinants of user behavior—is created. Second, this research confirms that the privacy paradox still exists. In the digital information age, most users will still choose to provide personal information in exchange for the services and applications they need. Trust, social cognition, and culture play a vital role in intelligent societies and virtual interactions. Meanwhile, when technology applications can provide users with diversified and user-friendly functions, their perceived usefulness is significantly improved.

The structure of the article is as follows. In section “Theoretical Basis and Research Hypothesis,” we examine the theoretical basis and research hypothesis. Section “Variable Measurement and Data Collection” describes variable measurement and data collection, including questionnaire design and data collection. Section “Data Analysis” presents the results of the data analysis. Section “Conclusion” discusses the key findings of the research along with the final remarks.

Theoretical Basis and Research Hypothesis

In the era of mobile data services based on big data, “the nature of economic exchange is more inclined to exchange personal information for personalized services. Privacy violations may occur in the acquisition, storage, use and transaction of personal information, thus giving rise to problems in information privacy” ( Chen and Cliquet, 2020 ). Moreover, in the Internet environment, information privacy security in intelligent society is increasingly threatened. Since facial recognition is based on the acquisition of human face image information and face information demands privacy, face information security becomes the focus of the public when choosing whether to use facial recognition technology. On the one hand, human faces are rich in features, which provide powerful biometric features for identifying individuals; thus, a third party can identify individuals through face positioning, and so it is necessary to prevent the malicious collection and abuse of such information. On the other hand, through image storage and feature extraction, a variety of demographic and private information can be obtained, such as facial age, health status, and even relatives, which leads to unnecessary privacy invasion ( Zahid and Ajita, 2017 ). Therefore, in view of the uniqueness of human face and information privacy, the focus of this paper will be whether the public’s actual use of face recognition is affected by their attitudes toward personal privacy and the perceived risk of personal data.

Privacy Concerns

Privacy concerns are widely used to explain the behavior intention of users ( Zhang and Li, 2018 ). In the Internet field, privacy concerns of users include people’s perceptions and concerns about improper access, illegal acquisition, illegal analysis, illegal monitoring, illegal transmission, illegal storage, and illegal use of private information ( Wang et al., 1998 ). Users do not have full control over the use of their personal information. Thus, users become concerned about privacy when it may be violated due to security loopholes or inappropriate use or when individuals perceive the risk of privacy infringement.

Personal privacy in the age of mobile data services involves both online and offline domains. The extensive use of various personal biological information applications poses new challenges to personal privacy security. Specifically, with the progress of computer algorithms, the Internet of Things, and other technologies, the threshold of information collection becomes increasingly lower, and computerized information may be easily copied and shared, resulting in problems such as secondary data mining and inadequate privacy ( Qi and Li, 2018 ). In the existing research on privacy concerns, Cha found that there is a negative correlation between users’ concerns regarding the information privacy of a technology-driven platform and the frequency of users using the media ( Cha, 2010 ). McKnight conducted research on Facebook, whereby they found that the greater the concern about privacy is in a medium, the less willing people are to continue using the medium for fear of personal information being abused ( McKnight et al., 2011 ). In the context of big data, the privacy concerns of face recognition users originate from the risk of facial image information being collected and used without personal knowledge or consent or the risk of personal biometrics being transmitted or leaked. In other words, the cautious choice of face recognition application is influenced by the extent of individual concerns regarding privacy. Considering these notions, the following hypothesis is proposed:

Hypothesis 1: Privacy concerns have a negative impact on the actual use of face recognition.

Perceived Risk

Due to the virtuality or uncertainty of a network, perceived risk is an individual’s perception of the risk of information breach. The perceived risk of facial recognition may arise from the disclosure or improper use of face information. Chen conducted an empirical study on this and believed that the degree of individuals’ concerns for information security is affected by the perceived network risk ( Chen, 2013 ). Norberg et al. (2007) showed in their study that the negative effect of perceived disclosure is affected by perceived risk. In other words, the more users perceive that the disclosure of personal information will lead to the illegal breach of privacy and other adverse effects, the more they will be concerned about the security of personal privacy. Not only is the degree of privacy concerns positively affected by perceived risk, but studies have also shown that perceived risk also affects actual use behavior ( Zhang and Li, 2018 ). Hichang’s (2010) research results show that the degree of severity of privacy risks perceived by users is positively correlated with the degree of their self-protection behaviors. When people realize that their personal information is at risk, they take active preventive actions. Therefore, in this paper, regarding the intention to use facial recognition, it is believed that the higher the risk perceived by users, the more users will pay attention to the breach of personal privacy, thus affecting the actual use of facial recognition. In this vein, the following hypotheses are proposed:

Hypothesis 2: Perceived risk has a positive effect on privacy concerns.

Hypothesis 3: Perceived risk has a negative influence on the actual use of face recognition.

Trust Theory

Simmel (2002) pioneered the sociological study of trust, believing that trust is an essential comprehensive social force. Putnam (2001) believed that trust is an essential social capital and can improve social efficiency through actions that promote coordination and communication. In an intelligent social environment, social transactions cannot occur without trust. Hence, trust has also become an essential factor in the study of privacy issues. In the context of face recognition, trust is defined as users’ belief in the ability of face recognition technology and application platforms to protect their personal information. Joinson et al. (2010) found in his study that users’ perceived risk to personal privacy is affected by their degree of trust. Moreover, through research on the behavioral intention of intelligent media use, some scholars present that trust will directly affect the use intention, and there is a significant correlation between trust and users’ use intention. Therefore, the following hypotheses are proposed:

Hypothesis 4: Trust negatively affects the perceived risk of users with face recognition.

Hypothesis 5: Trust positively affects the actual use of face recognition.

Technology Acceptance Model

The TAM is widely used to explain users “acceptance of new technologies and products, and it is the most influential and commonly used theory to describe individuals” degree of acceptance to information systems ( Lee et al., 2003 ). The TAM is used for research in different fields: education ( Scherer et al., 2019 ), hospitals and healthcare ( Nasir and Yurder, 2015 ; Fletcher-Brown et al., 2020 ; Hsieh and Lai, 2020 ; Papa et al., 2020 ), sports and fitness ( Lunney et al., 2016 ; Lee and Lee, 2018 ; Reyes-Mercado, 2018 ), fashion ( Turhan, 2013 ; Chuah et al., 2016 ), consumer behavior ( Wang and Sun, 2016 ; Yang et al., 2016 ; Kalantari and Rauschnabel, 2018 ), gender and knowledge sharing ( Nguyen and Malik, 2021 ), wearable devices ( Magni et al., 2021 ), human resource management ( Del Giudice et al., 2021 ), Internet of Things ( Caputo et al., 2018 ), technophobia and emotional intelligence influence on technology acceptance ( Khasawneh, 2018 ).

In this study, a hypothesis model is developed based on perceived ease of use and perceived usefulness, two determinants of user behavior.

Perceived usefulness refers to the extent to which users believe that using a specific system will improve their job performance. Perceived ease of use refers to the ease with which users think a particular system can be used, which also affects their perceived usefulness of technology ( Davis, 1989 ). The easier it is to use face recognition, the more useful it is considered be. For the purpose of this study, face recognition aims to realize multiple functions, such as providing efficient and convenient services. Therefore, the definition of perceived usefulness should be extended to users think face recognition can improve the degree of convenience and service. In this paper, the ease of using a face recognition application refers to users’ perceived ease of use of the technology. Previously, Davis (1989) conducted an empirical study on the e-mail system and concluded that perceived ease of use has a positive impact on the use of applications. In a study on the adoption and use of information systems in the workplace, Venkatesh and Davis (2000) demonstrated that perceived usefulness has a positive impact on people’s usage behavior. With the extensive application of the TAM in the information system, the face recognition technology studied in this paper also comprises intelligent media. Perceived usefulness is an important variable that affects the use of face recognition. Thus, the following hypotheses are proposed:

Hypothesis 6: Perceived ease of use has a positive impact on perceived usefulness.

Hypothesis 7: Perceived ease of use has a positive influence on the actual use of face recognition.

Hypothesis 8: Perceived usefulness has a positive impact on the actual use of face recognition.

The research model of this paper is shown in Figure 1 .

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Figure 1. Structural equation model.

Variable Measurement and Data Collection

Questionnaire design.

In order to ensure the scientificity and credibility of the measurement variables, this study modified the mature scale in previous studies and combined it with the information concerns of current users on the use of face recognition and developed a questionnaire. This questionnaire consists of two parts. The first part investigates the demographic characteristics of users, such as gender and age. The second part is measured by a Likert scale. The options of each measurement item include “Strongly disagree,” “Disagree,” “Neither agree nor disagree,” “Agree,” and “Strongly agree.” The survey included seven latent variables and 21 measured variables. Latent variables included perceived ease of use, perceived usefulness, privacy concerns, risk perception, trust, and actual use. The contents of the scale are shown in Table 1 .

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Table 1. Design of measurement items for variables studied.

Data Collection

In this study, the questionnaire was designed on the survey platform 1 and distributed in the form of links through WeChat, QQ, and other channels. The survey was conducted from May 26 to June 10, 2020, and a total of 635 questionnaires were recovered. The subjects of the questionnaire were users of face recognition technology. After the second screening, 518 valid questionnaires remained after the elimination of incomplete questionnaires and all the questionnaires with the same options. The specific statistics are shown in Table 2 .

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Table 2. Statistical analysis of demographic characteristics ( N = 518).

From the reported statistics, it can be seen that the gender ratio of the sample data is balanced. The age structure of the respondents is mainly between 18 and 35 years old, so it is an overall young sample, conforming to the age characteristics of the main user group of facial recognition. The respondents mostly have a high level of education, with a bachelor’s degree or above. In terms of urban distribution, 58.7% of respondents came from first-tier cities and new first-tier cities. The sample coverage is reasonable and thus representative. As for privacy, more than 86.1% of respondents believe that face information is private. Consequently, the sample data collected in this questionnaire applies to the relevant research on the privacy problems of face recognition users.

Data Analysis

Reliability and validity analysis.

For this study, SPSS 25.0 was used to analyze the collected data and evaluate the reliability of the data. Cronbach’s alpha (α coefficient) was used to measure the data in this study. With 0.7 as the critical value, it is generally believed that when Cronbach’s α coefficient is greater than 0.7, the scale has considerable reliability. Based on the test results, the overall Cronbach’s α coefficients of privacy concerns, perceived risk, perceived ease of use, perceived use, trust, and actual use are between 0.876 and 0.907, all of which are greater than 0.7. This indicates that the measurement of each latent variable shows excellent internal consistency and that the questionnaire is reliable as a whole.

Structural validity refers to the corresponding relationship between measurement dimensions and measurement items. It is often used in research to analyze questionnaire items. According to the results of AMOS 24.0 for confirmatory factor analysis, the fitting index of confirmatory factor analysis in this study was X2/df = 2.722, which is less than 3, thus indicating that the fit was ideal. RMSEA = 0.058, which is less than 0.08, indicating that the model is acceptable. It is generally believed that when the fitting index of NFI, IFI, and CFI is greater than 0.9, it indicates that the model fits well; in this regard, NFI = 0.938, RFI = 0.925, IFI = 0.960, TLI = 0.951, CFI = 0.959. Therefore, the fitting index of this model conforms to the common standard, and the fitting degree of the model is proper.

Exploratory factor analysis is utilized to determine whether each measurement item converges to the corresponding factor, and the number of selected factors is determined by the number of factors whose eigenvalue exceeds 1. If the value of factor loading is greater than 0.6, it is generally considered that each latent variable corresponds to a representative subject ( Gerbing and Anderson, 1988 ; Gefen and Straub, 2005 ).

As shown in Table 3 , the values of factor loading of the latent variables, including privacy concerns, perceived risk, perceived ease of use, perceived usefulness, trust, and actual use, were all greater than 0.7, which shows that the corresponding topic of latent variables is highly representative.

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Table 3. Factor load and variable combination reliability.

Combined reliability (CR) and average variance extracted (AVE) were used for the convergent validity analysis. Generally, the recommended threshold of CR is greater than 0.8 or higher ( Werts et al., 1974 ; Nunnally and Bernstein, 1994 ). AVE is recommended to be above 0.5 ( Fornell and Larcker, 1981 ). As shown in Table 3 , the AVE of each latent variable was greater than 0.5, and CR was greater than 0.8, indicating that the convergence validity was ideal.

According to the results in Table 4 , there was a significant correlation between actual use and privacy concerns, perceived risk, perceived ease of use, perceived usefulness, and trust ( p < 0.001). In addition, the absolute value of the correlation coefficient corresponding to each variable was less than 0.5 and was less than the corresponding AVE square root. It indicates that there was a specific correlation between latent variables and a certain degree of differentiation among them, so the scale has an ideal level of discriminant validity.

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Table 4. Correlation coefficient and AVE square root.

Correlation Analysis

Correlation analysis studies whether there is a correlation between variables and uses the correlation coefficient to measure the degree of closeness between variables. The three statistical correlation coefficients are the Pearson correlation coefficient, the Spearman correlation coefficient, and the Kendall correlation coefficient, of which the Pearson correlation coefficient is commonly used in questionnaire and scale studies ( Qi and Li, 2018 ). In this study, SPSS 25.0 and Pearson’s correlation analysis were used to study whether there is a significant correlation between privacy concerns, perceived risk, perceived ease of use, perceived use, trust, and actual use in a hypothetical model to validate the validity of the research hypotheses.

Table 5 shows the means and standard deviations of privacy concerns, perceived risk, perceived ease of use, perceived usefulness, trust, and actual use and the Pearson correlation coefficient between the variables. From the mean, users had a higher perceived risk and a lower degree of trust. The results of correlation coefficient matrix showed that perceived risk and privacy concerns are significantly and positively correlated, and H2 was initially verified; privacy concerns, persistent risk, and actual use were negatively correlated ( r = –0.158, p < 0.01), and the correlation degree was weak, preliminarily supporting H1 and H3. There was a positive correlation between perceived ease of use, perceived usefulness, and actual use ( p < 0.01). Among these, perceived ease of use had a weak correlation with actual use ( r = 0.292) and perceived usefulness showed a moderate correlation with actual use ( r = 0.494); thus, H6, H7, and H8 were preliminarily verified. There was a significantly strong correlation between trust and actual use ( p < 0.01, r = 0.608), so H5 was preliminarily verified. In addition, trust was also negatively correlated with perceived risk, due to which H4 was preliminarily verified.

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Table 5. Correlation coefficient matrix and mean and standard deviation of variables.

Path Analysis and Hypothesis Testing

The correlation analysis results showed that there was a correlation between the variables, so these hypotheses were preliminarily supported. Nevertheless, it could not adequately explain the systematic relationship between variables. Thus, AMOS 24.0 and the structural equation model were further employed in this study to explore the systematic relationship between the variables. As shown in Figure 2 .

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Figure 2. Path analysis diagram of the structural equation model.

As can be seen from Table 6 , the ratio of chi-square to the degree of freedom in the structural equation was less than 5, which is within the acceptable range. RFI, CFI, NFI, TLI, IFI, and GFI indexes were all significantly greater than 0.9, and the root mean square error of approximation (RMSEA) was less than 0.08. Thus, it shows that the structural equation model fits well.

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Table 6. Fitting of the structural equation model ( N = 518).

According to Table 7 , the hypotheses H2, H4, H5, H6, and H8 were verified, which shows that trust and perceived usefulness both positively influence the actual use intentions of face recognition users and that perceived risk also has a significant positive impact on privacy concerns. This indicates that the higher the public’s awareness of privacy is, the more risks it will perceive and the higher the public’s concerns about privacy will be. However, H1 and H3 were not accepted. From the test results, it can be seen that privacy concerns and perceived risk had a negative influence on the actual use of face recognition, but the influence was not significant. In addition, H7 was not supported, indicating that perceived ease of use had no significant influence on the actual use of face recognition.

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Table 7. Results of the hypothesis test.

Hypotheses H1, H3, and H7 were not supported for the following reasons:

1. H1 and H3 were not supported: Perceived risk and privacy concerns had no significant adverse effect on the actual use of face recognition. It shows that the public chooses to use face recognition despite their concern and perception of privacy and risk. Some scholars have called this contradictory phenomenon a privacy paradox ( Xue et al., 2016 ). In other words, although users are worried that face recognition may lead to improper use or disclosure of personal information, they still choose to use face recognition in the field of mobile networks. An important reason is that the application of intelligent media technology, facial recognition, is becoming increasingly prevalent in our daily lives, which is reflected in all aspects of our lives. Especially in the field of public services, relying on the digital platform has improved effectiveness and efficiency via face scanning.

2. H7 was not supported: The positive influence of perceived ease of use on the actual use of face recognition was not significant. This conclusion is not consistent with previous research, but to some extent, it confirms the correlation between perceived ease of use and the use of information systems. In other words, since ease of use involves self-efficacy cognition, technology anxiety can make users perceive it to be difficult to operate and reduce their evaluation of the ease of use of the system, thus further affecting the use of face recognition technology ( Bhattacherjee, 2001 ). Affected by external factors such as light and image clarity, the maturity of face recognition technology is not high, and the algorithm is not accurate, which affects the public’s perceived ease of use. It also reflects that, for the face recognition technology, perceived usefulness has a more substantial impact on the actual use, and those users value the functional benefits brought by face recognition applications.

Robustness Test of the Model

In this paper, gender, age, educational background, and city of the respondents were introduced into the model as control variables to test the robustness of the hypothesis model. The test results are shown in the figure below.

It can be seen from Figure 3 that despite introducing control variables, such as gender, age, education background, and city, the relationship and significance level of each factor of the model were consistent with the conclusion of hypothesis test results above. Meanwhile, the test results of the influence of each control variable on the actual use of face recognition were not significant, indicating that the model passed the robustness test.

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Figure 3. Robustness test.

In this study, taking the users of face recognition as the research objects, the TAM was integrated, and variables such as privacy concerns, perceived risk, and trust were added to the model to analyze the mechanism of how they affect the actual use of face recognition and explain the determinants for the use of facial recognition by the public. The results showed that the model fit well and that most of the hypotheses were supported.

Based on the results of the model analysis, this paper draws the following conclusions:

1. In the context of big data, the concept of information privacy has been continuously expanded. When users perceive the risk of their private information being disclosed through face recognition, they will have greater privacy concerns. However, although users’ privacy concerns are deep, the privacy paradox still exists. In the digital information age, most users will still choose to provide personal information in exchange for the services and applications they need.

2. Trust plays a vital role in intelligent societies and virtual interactions. In this paper, users’ trust in face recognition applications includes trust in the technology application platforms and trust in the face recognition technology itself. This study shows that the trust of technology and platform will reduce the user’s intention to safeguard themselves against it. Users believe that face recognition platforms can provide secure conditions for the use of the technology, and thus, they show a higher tendency to use such technology. On the other hand, users’ trust in face recognition technology improves, so their perceived risk of privacy information leakage is significantly reduced. In this regard, in the information age, users are willing to disclose personal information more out of their trust in face recognition technology and the related platforms.

3. In the context of face recognition as an emerging technique, the TAM still has excellent explanatory power. Although perceived ease of use has no significant positive impact on the actual use of face recognition due to other external factors, such as accuracy and technology maturity, perceived usefulness still has a significantly positive impact on the actual use of face recognition. To an extent, when technology applications can provide users with diversified and user-friendly functions, their perceived usefulness will be significantly improved.

4. The final consideration is the use management of government and technical ethics of enterprises. When developing face recognition, enterprises must pay attention to technical ethics, as well as privacy, to ensure personal privacy and protect against biological information leakage. The government must also strengthen its management of face recognition technology on a large scale to prevent enterprises and individuals from using technology to affect social security and personal privacy.

Limitations

There are some limitations to this study. First, the sample data in the model are mostly from a young group. In future research, survey data of other age groups can be explored to discuss whether the privacy concerns of users of different age groups will affect their use of facial recognition. Second, this study focuses on the influence of privacy concerns, perceived risk, perceived ease of use, perceived usefulness, and trust on the actual use of face recognition but has not assessed whether other factors, such as user experience and usage habits, affect the actual use of face recognition. In addition, this study only analyzes the direct impact of the research variables on the actual use but fails to account for the impact of the mediating variables or moderator variables.

Future Research Directions

Although this research provides some interesting insights, it has some significant limitations. First, future research should conduct research on different age groups to study the acceptance of face use and attention to privacy at different ages.

Second, privacy is one of the most critical ethical issues in the era of mobile data services. In the current age dominated by big data, privacy issues have become more prominent due to over-identification, technical flaws, and lagging legal construction. In this information era, the connection characteristics of the Internet pose a particularly unique information privacy threat, and many databases and records have led to the privacy boundary continually expanding. How do we balance technological enabling with privacy protection? What should users do about the privacy paradox? The different social cultures and psychology between China and the West cause people to use face recognition differently.

In terms of the impact of Western culture on face recognition, the culture pays attention to privacy and freedom, and politics and social culture affect the use of face recognition. The error and discrimination of face recognition algorithm will cause great psychological harm, coupled with the impact of social culture, and lead to social contradictions. For example, after testing the face recognition systems of Microsoft, Facebook, IBM, and other companies at MIT, it was found that the error rate of women with darker skin color is 35% higher than that of men with lighter skin color. In this regard, the algorithm was suspected to exhibit gender and racial discrimination. The algorithm is designed by people. Developers may embed their values in the algorithm, so there are artificial bias factors, which will lead to social contradictions. Therefore, politics, society, and culture have affected the governance attitude of the West. In terms of social background, religious contradictions and ethnic contradictions in Western society have intensified, and ethnic minorities have been discriminated against for a long time. The West is highly sensitive to prejudices caused by differences in religious beliefs, ethnic groups, and gender. Culturally and psychologically, the West attaches great importance to personal privacy and absolute freedom. Europeans regard privacy as dignity, and Americans regard privacy as freedom. These are some of the new problems we should focus on resolving now.

The core element of cognitive science is cognition, which is also known as information processing. Cognitive science and artificial intelligence are closely linked. The American philosopher J.R. Searle indicated that in the history of cognitive science, computers are key. Without digital computers, there would be no cognitive science ( Baumgartner and Payr, 1995 ). It is particularly important in the research of face recognition and cognitive science. Whether people use face or not has a great relationship with their cognition, consciousness, psychology, and culture. The global workspace theory of Baars, a psychologist, posits that the brain is a modular information processing device composed of many neurons, and the information processing process is composed of different neurons with different divisions of labor and functions. The distributed operation process of specialized modules. The rapidly changing neuronal activity process constructs a virtual space called the global workspace at any given time through competition and cooperation between modules. Consciousness and unintentional state are generated through competition in the workspace. The generation of consciousness refers to all specialized modules in the brain responding to these new stimuli at the same time and analyzing and integrating this stimulus information in the global workspace through competition and cooperation until the best matching effect is achieved in the information processing between modules ( Baars, 1988 ). Andrejevic and Volcic (2019) believes that exposing his face to the machine is in the interest of “efficiency” in this new world situation, creating contradictions with religious and cultural traditions. Face recognition largely depends on the exact meaning given to them by a wide range of actors, such as government, businesses, and civil society organizations ( Norval and Prasopoulou, 2017 ).

Finally, there is the consideration of face recognition and privacy management. Governments and enterprises should strengthen the management and design of face recognition technology. The technology itself is neutral, and the intelligent measures developing from online to offline, as in the case of facial recognition, are targeted at efficient, convenient, and humanized services. Thus, the public must be willing to disclose their personal information to experience the benefits of the use of intelligent media entirely. As scholars have declared, “It is the default transaction rule in the data age to give up part of privacy for the fast operation” ( Mao, 2019 ). Therefore, for the technology of face recognition at a crossroads, on the one hand, one cannot give up the application of technology because of privacy security. Instead, we should rely on smart hardware systems to empower cities and life with innovative technologies.

On the other hand, we cannot abuse this facial recognition technology after only viewing its bright prospects. Data security is always a crucial factor. Therefore, we believe that for face recognition technology, we must balance security, convenience, and privacy, strengthen the research on privacy issues in the field of big data networks, pay attention to the data flow behind it, constrain the technology with other evolving technologies, and cultivate the privacy literacy of the public.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Ethics Statement

The studies involving human participants were reviewed and approved by the Secretariat of Academic Committee, Hangzhou Dianzi University. The participants provided their written informed consent to participate in this study.

Author Contributions

TL and BY: conceptualization, software, and formal analysis. TL, SD, and YG: methodology and validation. TL and SD: investigation, resources, and data curation. TL, YG, and BY: writing—original draft preparation and visualization. TL, BY, and SD: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

We are grateful for the financial support from the Zhejiang Social Science Planning “Zhijiang Youth Project” Academic Research and Exchange Project: Social Science Research in the Era of AI (22ZJQN06YB), the Special Fund of Fundamental Research Funds for Universities Directly Under the Zhejiang Provincial Government (GK199900299012-207), and Excellent Backbone Teacher Support Program of Hangzhou Dianzi University (YXGGJS).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We are highly appreciative of the invaluable comments and advice from the editor and the reviewers.

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Keywords : face recognition, technology acceptance model, social cognitive, cross-culture, privacy concerns psychology, perceived risk, trust, cultural psychology

Citation: Liu T, Yang B, Geng Y and Du S (2021) Research on Face Recognition and Privacy in China—Based on Social Cognition and Cultural Psychology. Front. Psychol. 12:809736. doi: 10.3389/fpsyg.2021.809736

Received: 05 November 2021; Accepted: 06 December 2021; Published: 24 December 2021.

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Copyright © 2021 Liu, Yang, Geng and Du. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Tao Liu, [email protected]

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Title: a survey of face recognition.

Abstract: Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023.

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Research on face recognition algorithm based on image processing.

College of Information and Communication Engineering University, Harbin 150001, Heilongjiang, China

Zhenyun Ren

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While network technology is convenient for our daily life, the problems that are exposed are also endless. The most important thing for everyone is information security. In order to improve the security level of network information and identify and detect faces, the method used in this paper has improved compared with the traditional AdaBoost method and skin color method. AdaBoost detection is performed on the image, which reduces the probability of false detection. The experiment compares the experimental results of the AdaBoost method, the skin color method and the skin color + AdaBoost method. All operations in the KPCA and KFDA algorithms are performed by the inner product kernel function defined in the original space, and no specific non-linear mapping function is involved.The full name of KPCA is kernel principal component analysis. The full name of KFDA is kernel Fisher discriminant analysis. Combining the zero-space method kernel discriminant analysis method improves the ability of discriminant analysis to extract non-linear features. Through the secondary extraction of PCA features, a better recognition result than the PCA method is obtained. This paper also proposes a zero-space based Fisher discriminant analysis method. Experiments show that the zero-space-based method makes full use of the useful discriminant information in the zero space of the intraclass dispersion matrix, which improves the accuracy of face recognition to some extent.If you choose the polynomial kernel function, when d  = 0.8, KPCA has a higher recognition ability. When d  = 2, the recognition rate of KFDA and zero space-based KFDA is the largest. For polynomial functions, in general, d  = 2.

1. Introduction

In recent years, especially in areas where some terrorist attacks have been raging, various types of information identification and detection have become of great importance. Due to their own broad application areas, as well as false detections and missed inspections, it will cause our public safety. Great threats, these characteristics make information detection and recognition become more important. After the continuous exploration and practice of human beings, the emergence of biometric-based face detection and recognition technology has attracted the attention of most people. Because the biological signs are not interfered by external conditions, the formation itself is also determined by the individual's genes. The individual's genes themselves are unique and cannot be forged, and are well received by everyone. No matter which type of biological certificate, it has its own uniqueness. These uniquenesses are inherently formed by individuals and cannot be trained and forged. Later, the more mature computer image processing technology can basically detect and identify these biological signs.

Although humans can detect and identify a person from the face without difficulty in the case of great changes in expression, age, or hairstyle, it is very difficult to establish a system that can fully automate face recognition. It involves a lot of knowledge in pattern recognition, image processing, computer vision, psychology, etc., and is closely related to the identification methods based on other biometrics and computer human-computer perception interaction. At this time, computer vision technology began to enter people's field of vision as the world developed.

In order to improve the security level of network information, the face is identified and detected. In this paper, the combination of skin color and AdaBoost is used. The previous experiments and the analysis of skin color features can eliminate the complex background of nonface and perform AdaBoost detection on images. All operations in the KPCA and KFDA algorithms are performed by the inner product kernel function defined in the original space, and no specific non-linear mapping function is involved. This is the core technique of the kernel learning method. The KPCA and KFDA algorithms can be described in the same framework by constructing a corresponding linear feature space, then projecting the image into this linear space and using the resulting projection coefficients as the identified feature vector.

The introduction part of the article explains the research background and significance of the article and the research status at home and abroad. The method part describes the concept and algorithm of face detection and recognition model. The experimental part describes the data source and parameters. Conclusion The experimental data are analyzed in the Discussion section.

2. Literature Review

Based on the uniqueness and advancement of face detection and recognition technology, many research teams at home and abroad have begun in-depth research. In [ 1 ], the author proposes a new face SR method based on Tikhonov regularized neighborhood representation (TRNR). It can overcome the technical bottleneck of the patch representation scheme in the traditional neighbor-embedded image SR method. In [ 2 ], the authors evaluated methods using automated face detection techniques to help estimate site use for two chimpanzee communities trapped by cameras. The authors used a traditional manual inspection lens as a baseline to analyze the basic parameters specific to the change to evaluate the performance and practical value of chimpanzee face detection software. In [ 3 ], the author describes an automatic parking system that includes a camera mounted at the entrance/exit of the parking lot. If the camera keeps getting frames and detects faces, register them in the database. When the driver leaves, the facial image is captured again at the exit of the parking lot and compared in the database to arrive at the identity. In [ 4 ], the author first used the recently introduced 300 VW benchmark to fully evaluate the most advanced deformable face tracking pipeline. Afterwards, many different architectures were evaluated, focusing on the task of online deformable face tracking. In particular, the authors compared the following general strategies: (a) universal face detection plus general facial landmark positioning, (b) universal model free tracking plus generic facial landmark positioning, and (c) mixed state of use state art face detection, model free tracking and facial landmark positioning technology. In [ 5 ], the authors propose a method of learning salient features that responds only in the face area. Based on the salient features, the authors have also designed a joint pipeline for detecting and recognizing faces as part of the human-computer interaction (HRI) system of SRU robots. In the experiment, the article analyzes the influence of the saliency term on facial verification and the ability to discriminate against the significant features of LFW. And the experimental results of FDDB verify the effectiveness of the proposed method in face detection. In [ 6 ], the author solves these problems by proposing a new video steganography method based on Kanade-Lucas-Tomasi (KLT) tracking using Hamming codes (15, 11). Experimental results show that the method achieves higher embedding capacity and better video quality. In addition, compared with the prior art methods, the proposed algorithm improves the security and robustness of the face detection method. In [ 7 ], the authors propose a face recognition system based on low-power convolutional neural network (CNN) for user authentication in smart devices. The system consists of two chips: an always-on function CMOS image sensor (CIS) for imaging and face detection and a low-power CNN processor (CNNP) for face verification (FV). The results of the study show that the function CIS integrated with the FD accelerator can realize event-driven chip-to-chip communication of the face image only when there is a face.

In order to deeply study the characteristics and advancement of computer vision technology, many research teams at home and abroad have applied computer vision technology in different fields. In [ 8 ], the author applies computer vision technology to deep learning, and the article briefly outlines some of the most important deep learning programs used in computer vision problems. The authors briefly introduce their history, structure, strengths and limitations, then describe their application in various computer vision tasks, and briefly outline the future direction of designing deep learning solutions for computer vision problems and the challenges involved. In [ 9 ], the author applies computer vision technology to image classification, describing the steps involved in quantifying microscopic images and the different methods for each step. The authors used modern machine learning algorithms to classify, cluster, and visualize cells in HCS experiments. In addition to classification or clustering tasks, machine learning algorithms that learn feature representation have recently advanced the state of the art in several benchmarking tasks in the computer vision community. In [ 10 ], the author applied computer vision technology to the field of object recognition. Research shows that X-ray test research and development is exploring new methods based on computer vision that can be used to help operators. The article attempts to contribute to the field of object recognition in X-ray testing by evaluating different computer vision strategies proposed in the past few years. For each method, the author provides the results of the experiment displayed on the same database. In [ 11 ], the author applied computer vision technology to machine learning, especially support vector machine training, using 26 of the most common tree species in Germany as test cases, classifying specimen images, ideally at the species level. In [ 12 ], the author applied computer vision technology to cell segmentation and feature extraction. The authors outline common computer vision and machine learning methods for generating and classifying phenotype profiles, and the need for effective computational strategies for analyzing large-scale image-based data is increasing. Computer vision methods have been developed to aid in phenotypic classification and clustering of data acquired from biological images. In [ 13 ], the author applied computer vision technology to visual inspection systems, introduced a vision system for automatic measurement and detection of most types of threads, and developed many image processing and computer vision algorithms to analyze captured. In [ 14 ], the authors applied computer vision techniques to type annotations, describing the types of annotations that computer vision researchers use to crowdsource collection, and how they ensure that the data is of high quality while minimizing annotation work. Finally, the author summarizes the future of crowdsourcing in computer vision.

In [ 15 ], elated researchers have proposed a powerful framework, named Kernel-norm-based adaptive occlusion dictionary learning, for face recognition with illumination changes and occlusions. Experiments on multiple public datasets show that the NNAODL model can achieve better results than classical methods in the presence of occlusion and illumination changes. In [ 16 ], elated researchers propose a novel coupled similarity reference encoding model for age-invariant face recognition by combining nonnegatively constrained reference encoding with coupled similarity measure. Experiments using deep features are performed and high recognition rates are achieved, which shows that the model can be combined with deep networks for better results. In [ 17 ], the authors perform some implementations and comparisons of classifiers and 2D sub-space projection methods for face recognition problems. Experimental results show that using these feature matrices with CMA, SVM and CNN in classification problems is more beneficial than using raw pixel matrices in terms of processing time and memory requirements. In [ 18 ], Most of the content consists of three parts, namely specific face representation, feature extraction and classification. The face representation represents how the face is displayed and determines the progressive algorithm for detection and recognition. Evaluate face recognition, which considers shape and texture data to talk to images based on local binary patterns for personal free face recognition. In [ 19 ], the authors aim to construct facial patterns stored in a digital image database. The process of pattern construction and face recognition starts with an object in the form of a face image, side detection, pattern construction until the similarity of face patterns can be determined, and then face recognition. In this study, a program was designed to test some samples of face data stored in a digital image database so that it could provide similarity of observed face patterns and introduced them using PCA.

3.1. Face Detection and Recognition Model Based on DeepID

3.1.1. network structure.

The network structure of the Deep ID network is similar to the most basic convolutional neural network. In the Deep ID network structure, the main role of the convolutional network is to classify trained faces. The convolutional network here consists of 4 convolutional layers and 3 pooling layers, and the characteristics of the samples are represented by the last layer in the network. Using a picture as the input of the Deep ID network, the low-level features of the picture are extracted by the lower layer network and calculated layer by layer by convolution, so that the number of extracted features is gradually reduced, and the global structure of the network structure is enhanced. Sex, and can form advanced features in the top-level network structure. The Deep ID network will eventually output an advanced vector of dimension 160, which is highly dense and contains a wealth of authentication information that can be used directly for identification.

3.1.2. Calculation Process

As mentioned before, the network structure of Deep ID includes 4 convolutional layers and 3 pooling layers. Among them, after the first three convolution layers, there is a pooling layer. Behind the fourth convolutional layer, the structure implementation is directly connected to the fully connected layer and through this layer forms an output layer and features for classification. The input picture is divided into categories such as scale, channel, range, etc., and the training process of each vector is relatively independent. Finally, all vectors are connected to obtain the final vector.

3.1.3. Joint Bayesian Model

The joint Bayesian model is also widely used in the field of face recognition. The idea of joint Bayesian model comes from Bayesian face recognition, which mainly consists of dividing a face into two parts, one part is human and human. The difference between the parts is the difference of the individual itself, such as the difference caused by external conditions such as expressions and angles. Then, there are

In the formula, μ ∝ N (0, S μ ), represents the difference between people, that is, external differences; ε ∝ N (0, S ε ), represents the difference of the individual itself due to other factors, that is, internal differences. Also assume that both parts are subject to a Gaussian distribution. And by calculating the covariance of the two parts, you can get

At this point, the EM iteration of the above formula can get the similarity between the two:

3.2. AdaBoost Face Recognition Algorithm Based on Skin Color Segmentation

3.2.1. color space.

The RGB color space is a color space established by using three kinds of monochromatic light of red (700.0 nm), green (546.1 nm), and blue (453.8 nm) as a coordinate system. According to the principle of three primary colors, in the RGB color space, any color light F can be expressed as

The RGB color space is based on a Cartesian coordinate system. The three axes of the three-dimensional space correspond to the three primary colors respectively. The coordinate origin corresponds to black, and the corresponding three components R , G , and B are zero. The origin diagonal corresponds to white, and the corresponding three components R , G , and B . The maximum, the three components of the two points on the line R , G , B are equal, corresponding to the gray pixel points, that is, the gray line. The remaining three vertices of the cube correspond to cyan at R  = 0, purple at G  = 0, and yellow at B  = 0. In RGB space, if the value of two pixel points [ R 1 , G 1 , B 1 ], [ R 2 , G 2 , B 2 ] are proportional, i.e.,

The above formula shows that such proportional points have the same color and different brightness, and by normalization, the chrominance components can be removed to obtain the [ r , g , b ] space, namely,

3.2.2. Skin Color Segmentation

According to the skin color samples in the YCrCb space, the mean value C b , the mean m of the C r , and the covariance matrix C are calculated by the following formula to obtain

The number of skin color pixels counted by N in the above two formulas. Finally, the Gaussian skin color model is defined by the elliptic Gauss joint probability density function:

where x is the color vector, m and C are the average vector and the covariance matrix, respectively. The probability of the function P ( x |skin) is the skin color similarity of each pixel, which can be used to determine whether it is skin color. Finally, through the threshold setting, an image of the skin color segmentation is obtained. The mean and variance are

In this paper, the threshold method is used to realize simple and fast calculation. It is a commonly used image binarization method. The essence is to use statistical information to determine the segmentation threshold for segmentation. Accurate segmentation of the skin to the image is accomplished by a suitable threshold during this process. We know that different human skin colors can form clusters in the YCrCb space, which provides a basis for skin color segmentation. By creating a skin color model in the YCrCb space, the skin color segmentation is performed using two components, C b and C r . In this way, the YCrCb space can be obtained by linearly transforming from RGB space, which is simple and fast. Secondly, the Y component of the luminance information is removed. At the same time, only two components are included, and the calculation speed is also high. Counting the range of skin to separate the skin from the nonskin area, you get

3.3. Face Recognition Algorithm Based on Linear Subspace

3.3.1. pca face recognition method.

Assuming that there are a total of M images in the original image library as training samples, the normalized images are connected by n  ×  n columns to form a 2 n -dimensional column vector. Then the original face image vector is represented as X1, X2,…, XM, and the average of the total face image is

The K-L transform is used to calculate the covariance matrix, also known as the overall dispersion matrix, namely,

In order to find the eigenvalues of the n 2 ×  n 2 dimensional matrix C and the orthogonal normalized eigenvectors U , the calculation is too large if the calculation is too large, thus introducing the singular value decomposition theorem (SVD theorem) to solve the problem of high dimensionality. That is, the matrix R  = AT A ( M  ×  M dimensional matrix) is calculated first, and the orthogonal normalized eigenvectors V of R is calculated, and U and V have the following relationship:

3.3.2. Fisher Discriminant Analysis Face Recognition Method

(1) LDA Algorithm . Assuming that there are a total of N images in the original image library as training samples, the normalized image size is n  ×  n , and the columns are connected to form n 2 dimensional column vectors. Then the j th original face image vector of the i -th person is represented as X ij , where N i indicates that the N i face image belongs to the i -th class, and C is the sample class number. The average value of each type of face image is:

Defined according to the Fisher guidelines:

The optimum projection direction W is the value of W when the above formula reaches the maximum value. That is, W is the solution that satisfies the following equation:

That is, corresponding to the feature vector corresponding to the larger feature value of the matrix S-1WSb. Note that the matrix has a maximum of only C-1 non-zero eigenvalues, and C is the number of categories.

3.3.3. Zero Space Method

Direct linear discriminant analysis (D-LDA) first removes the null space of the interclass dispersion matrix S b , and finds a projection vector to minimize the intraclass dispersion, called D-LDA. The D-LDA method seems to avoid losing the zero space of S w . However, since the ranks of S b and S w have such a relationship: rank ( S b ) ≤ C − 1 ≤ rank ( S w ) ≤ N − C , removing the zero space of S b may result in the loss of part or all of the zero space of S w , which is likely to make S w full rank, that is, D-LDA indirectly loses S w . Zero space. The zero-space-based LDA first finds the null space of the intraclass dispersion matrix S w , and then projects the original sample onto the zero space of w S to maximize the interclass dispersion matrix S b . The optimal projection vector W should satisfy:

The optimal discriminant vector W should exist in the null space of w S .

3.4. Face Recognition Method Based on Kernel Method

3.4.1. kpca-based face recognition method.

First, the N training sample sets X1, X2, X3,…, XN in the original input space RN are nonlinearly mapped to the high-dimensional space by the polynomial kernel function to obtain the kernel matrix of the training set:

Then, the normalized kernel matrix K is calculated. Finally, the eigenvalues and eigenvectors of K are calculated, and the orthogonal eigenvectors corresponding to the largest m eigenvalues are u1, u2, u3,…, um. The sample after projection:

The above KPCA feature extraction is completed, and the sample Y in the high-dimensional space after the non-linear projection of the face is obtained, and input into the nearest neighbor classifier for classification and recognition.

3.4.2. KFDA-Based Face Recognition Method

After KPCA feature extraction, Y is a nonlinear mapping of faces to samples in high-dimensional space. The second feature extraction is performed on Y using the LDA algorithm. Calculate the best projection direction W according to the Fisher criterion function in the equation, and project Y to the optimal projection direction of the LDA.

The above KFDA feature extraction is completed, and K is extracted by KPCA and LDA features, and Z is input into the nearest neighbor classifier for classification and identification.

3.4.3. KFDA Face Recognition Algorithm Based on Zero Space (KFDA-NULL)

Set in the high-dimensional feature space, the interclass dispersion matrix and the intraclass dispersion matrix of the training samples are K b and K w , respectively, and the overall dispersion matrix is:

The total dispersion matrix K t in the feature space is calculated according to the input spatial data and the kernel function equation; then the eigenvalue analysis is carried out, and the transformation matrix P k is established by using the eigenvectors corresponding to all nonzero eigenvalues to obtain the intraclass dispersion after the feature space is reduced. Degree matrix and interclass dispersion matrix:

Enter any vector k j =[ k 1 j , k 2 j ,…, k Nj ] of the space, and formally project its projection into the high-dimensional feature space.

For large sample problems ( n  <  N ), S w is full rank and cannot extract any zero space. That is to say, in the case of large samples, any zero space based method fails. However, after nuclear mapping, the zero space based LDA can work on the core sample set. Therefore, for large sample problems, the kernel mapping method is an extension of the zero space method. Figure 1 is a flowchart of a face recognition system based on multi-feature fusion.

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Object name is CIN2022-9224203.001.jpg

Flow chart of face recognition system based on multifeature fusion.

4. Experiment

4.1. data source.

The experiments in this paper are mainly based on the public ORL face database and Yale face database. The ORL face database is one of the most widely used face libraries. The database consists of facial expressions and details of black faces on different periods of time. It consists of 40 people, each with 10 front-facing face images of 112  ∗  92 size. Some of them were taken at different times, and the lighting conditions were almost unchanged, most of them were changes in expressions and postures. For example: laughing or not laughing, blinking or closing your eyes, with or without glasses; face posture changes, rotation up to 20 degrees; face size also has up to 10% change. The Yale face library consists of 15 people, each with 11 front-facing faces of 128  ∗  128 size, including different expressions, different lighting, blinking or closing eyes, and wearing and not wearing glasses.

The training set used by Deep ID is Celeb Faces. Deep ID Network Training during the use of this data set, 80% of the data in the training set was used to train the neural network part of the Deep ID network, while the latter Bayesian model was completed by the remaining 20%. Celeb Faces is a large dataset with a total of 87,628 images from 5,436 famous people in the Celeb Faces dataset. This data set is ideally suited for use as a training set and test set for computer vision tasks, and can perform a variety of functions including face detection, facial features, and face recognition.

4.2. Experimental Parameter Setting

The AdaBoost training sample library created in this paper has 3000 face samples of 24 × 24 size, including nearly 700 multi-pose face samples with obvious deflection tilt. In the experiment, although the face samples are as much as possible, the face samples will be more good detection results, but at the same time will increase the training burden, so this paper selected 3000 face samples according to the previous detection system. As for the selection of 700 multi-pose faces, it has been better through experiments. Detection, but subsequent algorithm optimization needs to be thoroughly studied and compared. Although the use of skin color segmentation has excluded a large number of nonface background areas, in order to accurately detect the face, a large number of nonface samples are needed for training, so the “bootstrap” method is used to obtain the 5000 background images collected. A large number of non-face samples. The minimum detection rate m Din of the strong classifier is set to 0.999, the maximum false detection rate Fmax is 0.5, and the maximum number of training layers is 15.

5. Results and Discussions

5.1. analysis of single face recognition results based on skin color segmentation.

The test results of the single face test set are shown in Table 1 :

Single face test set test results.

According to the experimental test results and statistical analysis of the data in Table 1 :

The skin color feature is combined with the AdaBoost algorithm to eliminate the complex background of the human face under the gray image, which effectively reduces the false detection rate. However, we also find that the detection rate does not improve after the combination, but has a slight influence. By observing the test results, it was found that the detected image was severely affected by light and other factors, which led to misdetection or damage detected by the AdaBoost method.

As can be seen in Figure 2 , the introduction of skin color features into the AdaBoost algorithm is a good limitation of the number of false detection windows, but due to the leak detection of the skin color detection in the test image under weak lighting conditions, it is excluded that the light can be processed by illumination. The face detected by the AdaBoost method, so the skin color + AdaBoost method has more missed faces than the AdaBoost method, but in general, the introduction of skin color features greatly reduces the number of false detection windows, making the skin color + AdaBoost method in ROC The curve performance is better than the AdaBoost method. On this basis, although the method of this paper will also identify some weakly illuminated faces due to the introduction of skin color leakage, but because of the new sparse features instead of Haar features, the AdaBoost method has a better multi-pose face detection effect. Improvement, so that the overall detection efficiency of the algorithm is improved, but also for this reason, the false detection window of this method will be slightly more than the skin color + AdaBoost method.

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Object name is CIN2022-9224203.002.jpg

Single face test set ROC curve.

5.2. Analysis of Multi-Face Recognition Results Based on Skin Color Segmentation

The test results of the multi-face test set are shown in Table 2 :

Multi-face test set test results.

By observing Table 2 and the experimental data, we can get the conclusions roughly as shown in Table 1 , but compared with Table 1 , the false detection rate of the three methods is increased overall, and the detection rate is decreased. This is due to the multi-face image. The background may be more complicated, the face pose is more varied, even occluded and blurred, so the detection effect will be worse than the single face detection. But overall, the skin color + AdaBoost method of this article is still relatively better than the first two methods. Figure 3 shows the ROC curve for a self-built multi-face test set.

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Self-built multi-face test set ROC curve.

From Figure 3 , we can get similar conclusions as shown in Figure 2 , but because the background area in the multi-face image is more complicated and the face pose is more diverse, the test performance of the three methods is generally reduced. Overall, the method is still better. In the first two methods, the skin color feature is very good at limiting the false detection rate, and the sparse feature is better for detecting multi-pose faces.

5.3. Analysis of Face Recognition Results Based on Linear Subspace

The ORL library consists of 40 people. Each person has 10 different face images. The first 5 images of each person are used as the training set, and the last 5 images are used as test sets. The PCA method is used to select different feature space dimensions and different. The number of samples and the calculation recognition rate are shown in Table 3 .

Corresponding recognition rates of different feature sub-space dimensions and number of different training samples in PCA.

Where d is the dimension of the selected sub-space, n is the number of selected training samples (3 means 10 out of 10 images for one person, 7 for training), using the most recent neighbor classification. From the experimental data in the table, it is found that as the dimension of the sub-space increases, the recognition rate also increases accordingly. When the dimension of the subspace is small, the increase in the recognition rate is significant. At the same time, the number of training samples also has a great influence on the relationship between the sub-space dimension and the recognition rate. Select the same sub-space dimension, the more training samples, the higher the recognition rate. Therefore, the more samples used for training, the more adequate the training and the better the recognition. Of course, here we have to avoid the situation of training.

When the number of training samples is fixed, the higher the dimension of the sub-space, the higher the recognition rate. Observing a large amount of experimental data, we can see that the maximum value of recognition rate is about d  = 71 when n  = 7; the maximum value of recognition rate when n  = 5 is about d  = 80; when n  = 3, the recognition rate The maximum point is around d  = 90. After that, the sub-space dimension is increased and the recognition rate will not exceed this point. It can be seen that since the PCA method is based on gray scale statistics, some feature vectors may add invalid information such as noise, resulting in a decrease in the recognition rate. When the number of training samples per person is fixed at 5, the ORL face database increases with the dimension of the sub-space, and the changes of the threshold and recognition rate are shown in Figure 4 .

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PCA recognition rate changes with threshold.

When the dimension of the feature sub-space increases, the threshold of the feature value increases, and the recognition rate also increases. When the threshold is 0.65, the corresponding dimension is 12, and the recognition rate tends to be balanced. When the threshold is 0.92, the dimension is 80 and the recognition rate is the highest. In practical applications, the sub-space composed of the feature vectors corresponding to the feature values of 0.8∼0.9 of the overall feature value is generally used for PCA face recognition.

5.4. Face Recognition Method Based on Kernel Method

There are 40 people in the ORL, the first 5 images of each person are used for training, the last 5 are used for testing, the nearest neighbor method is used as the classifier, and different kernel functions and corresponding parameters are selected. The recognition results are shown in Table 4 . Table 5 shows the recognition results of different recognition methods in the three databases.

Identification results when selecting different kernel functions and corresponding parameters on the ORL face database.

Recognition results of different recognition methods in three databases.

If you choose the polynomial kernel function, you can see from Figure 5 that when d  = 0.8, KPCA has a higher recognition ability. Therefore, for KPCA, a polynomial kernel function with a small exponent (between 0 and 1) can achieve better recognition. However, for KFDA and zero-space-based KFDA (KFDA + NULL), the recognition rate is the highest when d  = 2, and KPCA also has a high recognition rate. When the value of d is from 0 to 2, the recognition rate of KPCA decreases. KFDA uses LDA for secondary feature extraction based on KPCA feature extraction. When KPCA recognition rate is quite high, it is equal to or close to 100. In the case of %, the secondary feature extraction using LDA will reduce the recognition rate; when the recognition rate of KPCA is relatively low, that is, KPCA cannot extract the discrimination information well, and the secondary feature extraction by LDA can effectively extract the discrimination information, so the recognition rate has increased. When d  = 2, the recognition rate of KFDA and zero space-based KFDA is the largest. For polynomial functions, in general, d  = 2.

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Comparison of several face recognition methods on ORL face database (using polynomial kernel function).

If the RBF kernel function is selected, σ 2 = 5 × 106, the above KPCA, KFDA and zero space-based KFDA three face recognition algorithms based on the kernel method have higher recognition ability. From the experimental data in Table 4 , it can be concluded that the face recognition algorithm based on the kernel method has good recognition performance when the RBF kernel function is selected and the parameter is set to σ 2 = 5 × 106. Figure 6 is a comparison diagram of four face recognition methods based on ICA.

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Comparison of four face recognition methods based on ICA.

The face is detected by the AdaBoost method, so the skin color + AdaBoost method has more missed faces than the AdaBoost method, but in general, the introduction of the skin color feature greatly reduces the number of false detection windows, making the skin color in the ROC The curve performance of +AdaBoost method is better than that of AdaBoost method. As the dimension of the feature sub-space increases, the threshold of the feature value increases, and the recognition rate also increases. When the threshold is 0.65, the corresponding dimension is 12, and the recognition rate tends to be balanced. When the threshold is 0.92, the dimension is 80, and the recognition rate is the highest. In practical applications, PCA face recognition usually uses a sub-space composed of eigenvectors corresponding to eigenvalues of 0.8 to 0.9 of the total eigenvalues. The three face recognition algorithms based on kernel method, KPCA, KFDA and KFDA based on null space, have high recognition ability. When the RBF kernel function is selected and the parameter is set to σ 2 = 5 × 106, the face recognition algorithm based on the kernel method has good recognition performance.

6. Conclusions

In order to identify and detect human faces through computer vision technology, this paper studies the algorithm and draws the following conclusions:

  • In general, the method used in this paper has improved compared with the traditional AdaBoost method and skin color + AdaBoost method. The introduction of the previous experiment and analysis of skin color features can better eliminate the complex background of non-face. AdaBoost detection is performed compared to directly using grayscale images, which reduces the probability of false detection. In addition, the new sparse features are used to replace the Haar features in the traditional AdaBoost algorithm, so that the system can better cope with the traditional AdaBoost method. Multi-pose face such as deflection tilt effectively reduces the face detection and improves the detection rate, but skin color features and sparse features improve the performance of the system at the same time.
  • Because the self-built face training sample has added the face collected by the laboratory itself, there is a high detection rate in the test. The experiment compares the AdaBoost method, the skin color method and the experimental results of the skin color + AdaBoost method. It can be seen that the method is better than the AdaBoost method in terms of detection rate and false detection rate. At the same time, it is also found that due to the addition of skin color features, it is possible to eliminate the face detected by AdaBoost due to illumination, etc., and the AdaBoost method using a new sparse feature for detecting the face of more gesture modes. It is more likely to cause false detections of complex backgrounds, but also because of the use of skin color features, these false detections are limited to the area of the skin color (such as the hand) background.
  • All operations in the KPCA and KFDA algorithms are performed by the inner product kernel function defined in the original space, and no specific non-linear mapping function is involved. This is the core skill of the nuclear learning method. Zero-space-based KFDA overcomes the effects of illumination and is robust to expression and attitude changes. The zero-space method can overcome the small sample problem in discriminant analysis by finding the best discriminant analysis information existing in the zero space of the interclass dispersion matrix. Combining the zero-space method kernel discriminant analysis method not only improves the ability of discriminant analysis to extract non-linear features, but also overcomes the small sample problem in discriminant analysis.
  • Through the secondary extraction of PCA features, a better recognition result than the PCA method is obtained. These two classical algorithms can be described by the same framework, that is, the corresponding linear feature space is constructed first, then the image is projected to the linear space, and the obtained projection coefficient is used as the identified feature vector. The only difference between the two methods is that the feature space is chosen differently. Aiming at the small sample problem of two linear sub-space methods, PCA and LDA, this paper also proposes a zero-space based Fisher discriminant analysis method. Experiments show that the zero-space-based method makes full use of the zero space in the intraclass dispersion matrix. The useful discriminating information improves the correct rate of face recognition to some extent.

Acknowledgments

This paper was funded by the National Natural Science Foundation of China under Grant no. 51679057.

Data Availability

Conflicts of interest.

The authors declare that there are no conflicts of interest regarding the publication of this article.

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