Purpose This study aims to explore whether face recognition technology – as it is intensely used by state and local police departments and law enforcement agencies – is racism free or, on the contrary, is affected by racial biases and/or racist prejudices, thus reinforcing overall racial discrimination. Design/methodology/approach The study investigates the causal pathways through which face recognition technology may reinforce the racial disproportion in enforcement; it also inquires whether it further discriminates black people by making (...) them experience more racial discrimination and self-identify more decisively as black – two conditions that are shown to be harmful in various respects. Findings This study shows that face recognition technology, as it is produced, implemented and used in Western societies, reinforces existing racial disparities in stop, investigation, arrest and incarceration rates because of racist prejudices and even contributes to strengthen the unhealthy effects of racism on historically disadvantaged racial groups, like black people. Practical implications The findings hope to make law enforcement agencies and software companies aware that they must take adequate action against the racially discriminative effects of the use of face recognition technology. Social implications This study highlights that no implementation of an allegedly racism-free biometric technology is safe from the risk of racially discriminating, simply because each implementation leans against our society, which is affected by racism in many persisting ways. Originality/value While the ethical survey of biometric technologies is traditionally framed in the discourse of universal rights, this study explores an issue that has not been deeply scrutinized so far, that is, how face recognition technology differently affects distinct racial groups and how it contributes to racial discrimination. (shrink)

There is increasing interest in clarifying how different face emotion expressions are perceived by people from different cultures, of different ages and sex. However, scant availability of well-controlled emotional face stimuli from non-Western populations limit the evaluation of cultural differences in face emotion perception and how this might be modulated by age and sex differences. We present a database of East Asian face expression stimuli, enacted by young and older, male and female, Taiwanese using the Facial (...) Action Coding System (FACS). Combined with a prior database, this present database consists of 90 identities with happy, sad, angry, fearful, disgusted, surprised and neutral expressions amounting to 628 photographs. 20 young and 24 older East Asian raters scored the photographs for intensities of multiple-dimensions of emotions and induced affect. Multivariate analyses characterized the dimensionality of perceived emotions and quantified effects of age and sex. We also applied commercial software to extract computer-based metrics of emotions in photographs. Taiwanese raters perceived happy faces as one category, sad, angry, and disgusted expressions as one category, and fearful and surprised expressions as one category. Younger females were more sensitive to face emotions than younger males. Whereas older males showed reduced face emotion sensitivity, older female sensitivity was similar or accentuated relative to young females. Commercial software dissociated six emotions according to the FACS demonstrating that defining visual features were present. Our findings show that East Asians perceive a different dimensionality of emotions than Western-based definitions in face recognition software, regardless of age and sex. Critically, stimuli with detailed cultural norms are indispensable in interpreting neural and behavioral responses involving human facial expression processing. To this end, we add to the tools, which are available upon request, for conducting such research. (shrink)

In face recognition systems, highly robust facial feature representation and good classification algorithm performance can affect the effect of face recognition under unrestricted conditions. To explore the anti-interference performance of convolutional neural network reconstructed by deep learning framework in face image feature extraction and recognition, in the paper, first, the inception structure in the GoogleNet network and the residual error in the ResNet network structure are combined to construct a new deep reconstruction network algorithm, (...) with the random gradient descent and triplet loss functions as the model optimizer and classifier, respectively, and it is applied to the face recognition in Labeled Faces in the Wild face database. Then, the portrait pyramid segmentation and local feature point segmentation are applied to extract the features of face images, and the matching of face feature points is achieved using Euclidean distance and joint Bayesian method. Finally, Matlab software is used to simulate the algorithm proposed in this paper and compare it with other algorithms. The results show that the proposed algorithm has the best face recognition effect when the learning rate is 0.0004, the attenuation coefficient is 0.0001, the training method is SGD, and dropout is 0.1, and the algorithm proposed in this paper has the largest mean average precision compared to other CNN algorithms. The correct rate of face feature matching of the algorithm proposed in this paper is 84.72%, which is higher than LetNet-5, VGG-16, and VGG-19 algorithms, the correct rates of which are 6.94%, 2.5%, and 1.11%, respectively, but lower than GoogleNet, AlexNet, and ResNet algorithms. At the same time, the algorithm proposed in this paper has a faster matching time and a higher correct matching rate than the joint Bayesian method, indicating that the deep reconstruction network algorithm proposed in this paper can be used in face image recognition, FE, and matching, and it has strong anti-interference. (shrink)

This paper analyses the features of the 2021 software for the creation of ultrarealistic digital characters “MetaHuman Creator” and reflects on the causes of such perceived effect of realism to understand if the faces produced with such software represent an actual novelty from an academic standpoint. Such realism is first of all defined as the result of semio-cognitive processes which trigger interpretative habits specifically related to faces. These habits are then related to the main properties of any realistic (...) face: being face-looking, face-meaning and face-acting. These properties, in turn, are put in relation with our interactions with faces in terms of face detection, face recognition, face reading and face agency. Within this theoretical framework, we relate the characteristics of these artificial faces with such interpretative habits. To do so, we first of all make an examination of the technological features behind both the software and the digital faces it produces. This analysis highlights four main points of interest: the mathematical accuracy, the scanned database, the high level of details and the transformative capacities of these artificial faces. We then relate these characteristics with the cultural and cognitive aspects involved in recognizing and granting meaning to faces. This reveals how metahuman faces differs from previous artificial faces in terms of indexicality, intersubjectivity, informativity and irreducibility. But it also reveals some limits of such effect of reality in terms of intentionality and historical context. This examination consequently brings us to conclude that metahuman faces are qualitatively different from previous artificial faces and, in the light of their potentials and limits, to highlight four main lines of future research based on our findings. (shrink)

The QR code recognition often faces the challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult, and therefore, to deal with this problem, artificial intelligence-based systems came into existence. To improve the recognition rate of QR image codes, this article adopts an improved adaptive median filter algorithm and a QR code distortion correction method based on backpropagation (BP) neural networks. This combination of artificial (...) intelligence algorithms is capable of fitting the distorted QR image into the geometric deformation pattern, and QR code recognition is accomplished. The two-dimensional code distortion is addressed in this study, which was a serious research issue in the existing software systems. The research outcomes obtained after emphasizing on the preprocessing stage of the image revealed that a significant improvement of 14% is observed for the reading rate of QR image code, after processing by the system algorithm in this article. The artificial intelligence algorithm adopted has a certain effect in improving the recognition rate of the two-dimensional code image. (shrink)

Face recognition is a computationally challenging classification task. Deep convolutional neural networks (DCNNs) are brain‐inspired algorithms that have recently reached human‐level performance in face and object recognition. However, it is not clear to what extent DCNNs generate a human‐like representation of face identity. We have recently revealed a subset of facial features that are used by humans for face recognition. This enables us now to ask whether DCNNs rely on the same facial information (...) and whether this human‐like representation depends on a system that is optimized for face identification. In the current study, we examined the representation of DCNNs of faces that differ in features that are critical or non‐critical for human face recognition. Our findings show that DCNNs optimized for face identification are tuned to the same facial features used by humans for face recognition. Sensitivity to these features was highly correlated with performance of the DCNN on a benchmark face recognition task. Moreover, sensitivity to these features and a view‐invariant face representation emerged at higher layers of a DCNN optimized for face recognition but not for object recognition. This finding parallels the division to a face and an object system in high‐level visual cortex. Taken together, these findings validate human perceptual models of face recognition, enable us to use DCNNs to test predictions about human face and object recognition as well as contribute to the interpretability of DCNNs. (shrink)

Emotion recognition plays an important role in intelligent human–computer interaction, but the related research still faces the problems of low accuracy and subject dependence. In this paper, an open-source software toolbox called MindLink-Eumpy is developed to recognize emotions by integrating electroencephalogram and facial expression information. MindLink-Eumpy first applies a series of tools to automatically obtain physiological data from subjects and then analyzes the obtained facial expression data and EEG data, respectively, and finally fuses the two different signals at (...) a decision level. In the detection of facial expressions, the algorithm used by MindLink-Eumpy is a multitask convolutional neural network based on transfer learning technique. In the detection of EEG, MindLink-Eumpy provides two algorithms, including a subject-dependent model based on support vector machine and a subject-independent model based on long short-term memory network. In the decision-level fusion, weight enumerator and AdaBoost technique are applied to combine the predictions of SVM and CNN. We conducted two offline experiments on the Database for Emotion Analysis Using Physiological Signals dataset and the Multimodal Database for Affect Recognition and Implicit Tagging dataset, respectively, and conducted an online experiment on 15 healthy subjects. The results show that multimodal methods outperform single-modal methods in both offline and online experiments. In the subject-dependent condition, the multimodal method achieved an accuracy of 71.00% in the valence dimension and an accuracy of 72.14% in the arousal dimension. In the subject-independent condition, the LSTM-based method achieved an accuracy of 78.56% in the valence dimension and an accuracy of 77.22% in the arousal dimension. The feasibility and efficiency of MindLink-Eumpy for emotion recognition is thus demonstrated. (shrink)

Face recognition depends upon the uniqueness of each human face. This is accomplished by the patterns formed by the unique relationship among face features. Unique face-patterns are produced by the intrusion of random factors into the process of biological growth and development. Processes are described which enable a unique face-pattern to be represented as a percept in the visual sensory system. The components of the face recognition system are analyzed as is the (...) manner in which the precept is connected through microcircuits to a memory file so that the history of a perceiver’s encounters with a familiar face enables the perceiver to access a memory store that is a record of the outcome of past encounters with the perceived. The importance of the face recognition system in enabling humans to individuate members the social group is discussed, as well as the importance of face recognition in the development of the individual’s social identity and ability to be a collaborative member of the social groups to which it belongs. The role of prosopagnosia—the inability to recognize familiar faces—in furthering an understanding of the face recognition system is examined, as is its importance in demonstrating the crucial nature of face recognition in human social functions. It is proposed that human face recognition is not a unique phenomenon but is an elaboration of processes existing in nonhuman primates as well as in lower animals. (shrink)

To optimize the weak robustness of traditional face recognition algorithms, the classification accuracy rate is not high, the operation speed is slower, so a face recognition algorithm based on local binary pattern and stacked autoencoder is proposed. The advantage of LBP texture structure feature of the face image as the initial feature of sparse autoencoder learning, use the unified mode LBP operator to extract the histogram of the blocked face image, connect to form the (...) LBP features of the entire image. It is used as input of the stacked AE, feature extraction is done, realize the recognition and classification of face images. Experimental results show that the recognition rate of the algorithm LBP-SAE on the Yale database has achieved 99.05%, and it further shows that the algorithm has a higher recognition rate than the classic face recognition algorithm; it has strong robustness to light changes. Experimental results on the Olivetti Research Laboratory library shows that the developed method is more robust to light changes and has better recognition effects compared to traditional face recognition algorithms and standard stack AEs. (shrink)

People recognize faces of their own race more accurately than faces of other races. The “contact” hypothesis suggests that this “other‐race effect” occurs as a result of the greater experience we have with own‐ versus other‐race faces. The computational mechanisms that may underlie different versions of the contact hypothesis were explored in this study. We replicated the other‐race effect with human participants and evaluated four classes of computational face recognition algorithms for the presence of an other‐race effect. Consistent (...) with the predictions of a developmental contact hypothesis, “experience‐based models” demonstrated an other‐race effect only when the representational system was developed through experience that warped the perceptual space in a way that was sensitive to the overall structure of the model's experience with faces of different races. When the model's representation relied on a feature set optimized to encode the information in the learned faces, experience‐based algorithms recognized minority‐race faces more accurately than majority‐race faces. The results suggest a developmental learning process that warps the perceptual space to enhance the encoding of distinctions relevant for own‐race faces. This feature space limits the quality of face representations for other‐race faces. (shrink)

Face recognition is one of the core and challenging issues in computer vision field. Compared to computer vision, human visual system can identify a target from complex backgrounds quickly and accurately. This paper proposes a new network model deriving from Where-What Networks (WWNs), which can approximately simulate the information processing pathways (i.e., dorsal pathway and ventral pathway) of human visual cortex and recognize different types of faces with different locations and sizes in complex background. To enhance the (...) class='Hi'>recognition performance, synapse maintenance mechanism and neuron regenesis mechanism are both introduced. Synapse maintenance is used to reduce the background interference while neuron regenesis mechanism is introduced to regulate the neuron resource dynamically to improve the network usage efficiency. Experiments have been conducted on human face images of 5 types, 11 sizes, and 225 locations in complex backgrounds. Experiment results demonstrate that the proposed WWN model can basically learn three concepts (type, location and size) simultaneously. The experiment results also show the advantages of the enhanced WWN-7 model for face recognition in comparison with several existing methods. (shrink)

A new three-order piecewise function was used to smoothen the model of Support Vector Machine( SVM) and a Third-order Piecewise Smooth SVM( TPSSVM) was proposed. By theory analyzing, approximation accuracy of the smooth function to the plus function is higher than that of the available. When dealing with the multi-class problem, a coding method of multi-class classification based on one-against-rest was proposed. Principal Component Analysis( PCA) was employed to extract the main features of face image set, and multi-class classification (...) of smooth SVM was used for face recognition. The experimental results on ORL and FERET face databases show that the recognition rate of smooth SVM for multi-class classification is better than the traditional identification methods. (shrink)

This paper presents a novel philosophical analysis of the problem of law enforcement’s use of biased face recognition technology in liberal democracies. FRT programs used by law enforcement in identifying crime suspects are substantially more error-prone on facial images depicting darker skin tones and females as compared to facial images depicting Caucasian males. This bias can lead to citizens being wrongfully investigated by police along racial and gender lines. The author develops and defends “A Liberal Argument Against Biased (...) FRT,” which concludes that law enforcement use of biased FRT is inconsistent with the classical liberal requirement that government treat all citizens equally before the law. Two objections to this argument are considered and shown to be unsound. The author concludes by suggesting that equality before the law should be preserved while the problem of machine bias ought to be resolved before FRT and other types of artificial intelligence are deployed by governments in liberal democracies. (shrink)

Two types of variables impact face recognition: estimator variables that cannot be controlled and system variables that are under direct control by the criminal justice system. This article addresses some of the reasons that eyewitnesses are prone to making errors, particularly false identifications. It provides a discussion of the differences between typical facial memory and eyewitness studies and shows that the two areas generally find similar results. It reviews estimator variable effects and focuses on system variables. Traditional facial (...) recognition researchers rarely study system variables but reveal important factors that police and policy makers should consider with regard to eyewitness identification and the courts. It concludes that there is still room for considerable improvement in identification procedures and wants to encourage more system variable research as a means of reducing wrongful convictions. (shrink)

We adapted the Bubbles procedure [Vis. Res. 41 (2001) 2261] to examine the effective use of information during the first 282 ms of face identification. Ten participants each viewed a total of 5100 faces sub-sampled in space–time. We obtained a clear pattern of effective use of information: the eye on the left side of the image became diagnostic between 47 and 94 ms after the onset of the stimulus; after 94 ms, both eyes were used effectively. This preference for (...) the eyes increased with practice, and was not solely due to the informativeness of the eyes for the task at hand. The bias for the eye on the left side of the image is explained in terms of hemispheric specialization. Although there were individual differences, most participants exhibited this pattern of effective use of information. An intriguing finding is that most participants displayed a clear sinusoidal modulation of effective use of attention through time with a frequency of about 10.6 Hz. (shrink)

Simulation process and mirroring mechanism appear to be necessary to the recognition of emotional facial expressions. Prefrontal areas were found to support this simulation mechanism. The present research analyzed the role of premotor area in processing emotional faces with different valence , considering both conscious and unconscious pathways. High-frequency rTMS stimulation was applied to prefrontal area to induce an activation response when overt and covert processing was implicated. Twenty-two subjects were asked to detect emotion/no emotion . Error rates and (...) response times were considered in response to the experimental conditions. ERs and RTs decreased in case of premotor brain activation, specifically in response to fear, for both conscious and unconscious condition. The present results highlight the role of the premotor system for facial expression processing, supporting the existence of two analogous mechanisms for conscious and unconscious condition. (shrink)

The ability to recognize a face is crucial for the success of social interactions. Understanding the visual processes underlying this ability has been the focus of a long tradition of research. Recent advances in the field have revealed that individuals having different cultural backgrounds differ in the type of visual information they use for face processing. However, the mechanisms that underpin these differences remain unknown. Here, we revisit recent findings highlighting group differences in face processing. Then, we (...) integrate these results in a model of visual categorization developed in the field of psychophysics: the RAP framework. On the basis of this framework, we discuss potential mechanisms, whether face-specific or not, that may underlie cross-cultural differences in face perception. (shrink)

Companies increasingly deploy artificial intelligence technologies in their personnel recruiting and selection process to streamline it, making it faster and more efficient. AI applications can be found in various stages of recruiting, such as writing job ads, screening of applicant resumes, and analyzing video interviews via face recognition software. As these new technologies significantly impact people’s lives and careers but often trigger ethical concerns, the ethicality of these AI applications needs to be comprehensively understood. However, given the (...) novelty of AI applications in recruiting practice, the subject is still an emerging topic in academic literature. To inform and strengthen the foundation for future research, this paper systematically reviews the extant literature on the ethicality of AI-enabled recruiting to date. We identify 51 articles dealing with the topic, which we synthesize by mapping the ethical opportunities, risks, and ambiguities, as well as the proposed ways to mitigate ethical risks in practice. Based on this review, we identify gaps in the extant literature and point out moral questions that call for deeper exploration in future research. (shrink)

We adapted the Bubbles procedure [Vis. Res. 41 (2001) 2261] to examine the effective use of information during the first 282 ms of face identification. Ten participants each viewed a total of 5100 faces sub-sampled in space–time. We obtained a clear pattern of effective use of information: the eye on the left side of the image became diagnostic between 47 and 94 ms after the onset of the stimulus; after 94 ms, both eyes were used effectively. This preference for (...) the eyes increased with practice, and was not solely due to the informativeness of the eyes for the task at hand. The bias for the eye on the left side of the image is explained in terms of hemispheric specialization. Although there were individual differences, most participants exhibited this pattern of effective use of information. An intriguing finding is that most participants displayed a clear sinusoidal modulation of effective use of attention through time with a frequency of about 10.6 Hz. (shrink)

In this paper I present the position that the use of face recognition technology (FRT) in law enforcement and in business is restrictive of individual autonomy. I reason that FRT severely undermines autonomous self-determination by hobbling the idea of freedom of the will. I distinguish this position from two other common arguments against surveillance technologies: the privacy argument (that FRT is an invasion of privacy) and the objective freedom argument (that FRT is restrictive of one's freedom to act). (...) To make this case, I suggest that autonomy itself is predicated on the possibility of acting ethically, of freely willing moral laws. I then claim that autonomous self-determination is established as self-determination via social interactions with others. If we conceptualize self-determination as a relation of establishing a claim to individual autonomy in a community of others, we can see how planned uses of FRT subvert possibilities for the establishment of socially recognized agency. FRT not only confuses the process of asking ethical questions but it also imposes the immanent likelihood that all actions are taken not by self-directed, free agents, but by passive subjects in the interest of abiding by the institutionally enforced law. (shrink)

In the context of coronavirus pandemic, the face-recognition-based access control system has been intensively adopted to protect students’ and teachers’ health and safety in school. However, the impact of FACS, as a new technology, on students’ attitude toward accepting FACS has remained unknown from the psychological halo effect. Drawn on “halo effect” theory where psychological effects affect the sense of social identity and belonging, the present study explored college students’ sense of school identity and belonging in using FACS (...) during COVID-19 based on the technology acceptance model. Data collected from 391 college students was analyzed using SEM to verify the relationship among perceived usefulness, perceived ease of use, intention to use, school identity, and school belonging. The results show that PU and PEU can positively predict IU, and consequentially can positively predict school identity and school belonging. Our study expands the application of halo effect theory to study FACS acceptance based on TAM, and provides strong evidence to support the effect of school FACS during the pandemic. The findings of this study also suggest that FACS acceptance can enhance students’ sense of school identity and belonging. (shrink)