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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

Previous studies have shown that social categorization can induce an own-group face recognition bias. However, similar and better other-group face recognition emerged recently. In this research, we aimed to examine whether competitive cues and group status accompanied by social categorization can modulate the inter-group face recognition bias. Moreover, we investigated how the group identification of individuals with different statuses affected the inter-group face recognition bias. The results indicated that an own-group face (...) recognition bias emerged for targets with in-group labels compared to out-group labels. Moreover, when the group labels signaled competitive cues, the own-group face recognition bias was reversed. Furthermore, low-status and similar-status individuals exhibited out-group face recognition bias, but high-status individuals did not. In addition, the higher the in-group identification scores of participants from the low-status group, the stronger the out-group face recognition bias. These results suggested that competitive cues would reverse the own-group face recognition bias and the group status would play a modulating role in face recognition bias. (shrink)

In functional magnetic resonance imaging studies, object selectivity is defined as a higher neural response to an object category than other object categories. Importantly, object selectivity is widely considered as a neural signature of a functionally-specialized area in processing its preferred object category in the human brain. However, the behavioral significance of the object selectivity remains unclear. In the present study, we used the individual differences approach to correlate participants’ face selectivity in the face-selective regions with their behavioral (...) performance in face recognition measured outside the scanner in a large sample of healthy adults. Face selectivity was defined as the z score of activation with the contrast of faces versus non-face objects, and the face recognition ability was indexed as the normalized residual of the accuracy in recognizing previously-learned faces after regressing out that for non-face objects in an old/new memory task. We found that the participants with higher face selectivity in the fusiform face area (FFA) and the occipital face area (OFA), but not in the posterior part of the superior temporal sulcus (pSTS), possessed higher face recognition ability. Importantly, the association of face selectivity in the FFA and face recognition ability cannot be accounted for by FFA response to objects or behavioral performance in object recognition, suggesting that the association is domain-specific. Finally, the association is reliable, confirmed by the replication from another independent participant group. In sum, our finding provides empirical evidence on the validity of using object selectivity as a neural signature in defining object-selective regions in the human brain. (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)

Research in face recognition has largely been divided between those projects concerned with front‐end image processing and those projects concerned with memory for familiar people. These perceptual and cognitive programmes of research have proceeded in parallel, with only limited mutual influence. In this paper we present a model of human face recognition which combines both a perceptual and a cognitive component. The perceptual front‐end is based on principal components analysis of face images, and the cognitive (...) back‐end is based on a simple interactive activation and competition architecture. We demonstrate that this model has a much wider predictive range than either perceptual or cognitive models alone, and we show that this type of combination is necessary in order to analyse some important effects in human face recognition. In sum, the model takes varying images of “known” faces and delivers information about these people. (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)

This article explores the political dimension of algorithmic face recognition through the prism of Gilles Deleuze and Félix Guattari’s notion of faciality. It argues that algorithmic face recognition is a technology that expresses a key aspect of contemporary capitalism: the problematic position of the individual in light of new forms of algorithmic and statistical regimes of power. While there is a clear relation between modern disciplinary mechanisms of individualization and the face as a sign of (...) individuality, in control societies this relation appears more as a contradiction. The article contends that Deleuze and Guattari’s concepts of machinic enslavement and social subjection offer a fruitful perspective from where to identify the power mechanisms behind the problematic position of the individual in the specific case of algorithmic face recognition. (shrink)