Journal articles: 'Face recognition ability'

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Rhodes, Gillian. "Adaptive Coding and Face Recognition." Current Directions in Psychological Science 26, no. 3 (June 2017): 218–24. http://dx.doi.org/10.1177/0963721417692786.

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Face adaptation generates striking face aftereffects, but is this adaptation useful? The answer appears to be yes, with several lines of evidence suggesting that it contributes to our face-recognition ability. Adaptation to face identity is reduced in a variety of clinical populations with impaired face recognition. In addition, individual differences in face adaptation are linked to face-recognition ability in typical adults. People who adapt more readily to new faces are better at recognizing faces. This link between adaptation and recognition holds for both identity and expression recognition. Adaptation updates face norms, which represent the typical or average properties of the faces we experience. By using these norms to code how faces differ from average, the visual system can make explicit the distinctive information that we need to recognize faces. Thus, adaptive norm-based coding may help us to discriminate and recognize faces despite their similarity as visual patterns.

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Richler, Jennifer J., R. Jackie Floyd, and Isabel Gauthier. "About-face on face recognition ability and holistic processing." Journal of Vision 15, no. 9 (July 29, 2015): 15. http://dx.doi.org/10.1167/15.9.15.

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Dennett, Hugh W., Elinor McKone, Mark Edwards, and Tirta Susilo. "Face Aftereffects Predict Individual Differences in Face Recognition Ability." Psychological Science 23, no. 11 (October 16, 2012): 1279–87. http://dx.doi.org/10.1177/0956797612446350.

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Ueno, Masataka, Hiroki Yamamoto, Kazunori Yamada, and Shoji Itakura. "Development and plasticity in face recognition ability." Proceedings of the Annual Convention of the Japanese Psychological Association 82 (September 25, 2018): 3PM—078–3PM—078. http://dx.doi.org/10.4992/pacjpa.82.0_3pm-078.

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JOSEPH, ROBERT M., KELLY EHRMAN, REBECCA MCNALLY, and BRANDON KEEHN. "Affective response to eye contact and face recognition ability in children with ASD." Journal of the International Neuropsychological Society 14, no. 6 (October 27, 2008): 947–55. http://dx.doi.org/10.1017/s1355617708081344.

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AbstractThis study tested the hypothesis that affective arousal in response to eye contact is negatively associated with face identification skills in children with autism spectrum disorder (ASD). Participants were 20 children and adolescents with ASD and 20 age- and IQ-matched typically developing (TD) children. Skin conductance response (SCR), a psychophysiological measure of autonomic arousal, was collected while participants viewed faces with gaze directed toward them and faces with gaze averted away from them. Participants also completed an independent match-to-sample face recognition test. Children with ASD exhibited significantly larger SCRs than TD children to faces with direct and averted gaze. There were no differences between SCRs to direct gaze and averted gaze in either group. Children with ASD exhibited a marginally significant decrease in face recognition accuracy relative to TD children, particularly when face recognition depended on the eye region of the face. Face recognition accuracy among children with ASD was negatively correlated with the amplitude of SCRs to direct gaze but not to averted gaze. There was no association between face recognition accuracy and SCRs to gaze in the TD group. These findings suggest that autonomic reactivity to eye contact may interfere with face identity processing in some children with ASD. (JINS, 2008, 14, 947–955.)

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Kramer, Robin S. S. "Forgetting faces over a week: investigating self-reported face recognition ability and personality." PeerJ 9 (July 16, 2021): e11828. http://dx.doi.org/10.7717/peerj.11828.

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Background Although face recognition is now well studied, few researchers have considered the nature of forgetting over longer time periods. Here, I investigated how newly learned faces were recognised over the course of one week. In addition, I considered whether self-reported face recognition ability, as well as Big Five personality dimensions, were able to predict actual performance in a recognition task. Methods In this experiment (N = 570), faces were learned through short video interviews, and these identities were later presented in a recognition test (using previously unseen images) after no delay, six hours, twelve hours, one day, or seven days. Results The majority of forgetting took place within the first 24 hours, with no significant decrease after that timepoint. Further, self-reported face recognition abilities were moderately predictive of performance, while extraversion showed a small, negative association with performance. In both cases, these associations remained consistent across delay conditions. Discussion The current work begins to address important questions regarding face recognition using longitudinal, real-world time intervals, focussing on participant insight into their own abilities, and the process of forgetting more generally.

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Tardif, Jessica, Xavier Morin Duchesne, Sarah Cohan, Jessica Royer, Caroline Blais, Daniel Fiset, Brad Duchaine, and Frédéric Gosselin. "Use of Face Information Varies Systematically From Developmental Prosopagnosics to Super-Recognizers." Psychological Science 30, no. 2 (November 19, 2018): 300–308. http://dx.doi.org/10.1177/0956797618811338.

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Face-recognition abilities differ largely in the neurologically typical population. We examined how the use of information varies with face-recognition ability from developmental prosopagnosics to super-recognizers. Specifically, we investigated the use of facial features at different spatial scales in 112 individuals, including 5 developmental prosopagnosics and 8 super-recognizers, during an online famous-face-identification task using the bubbles method. We discovered that viewing of the eyes and mouth to identify faces at relatively high spatial frequencies is strongly correlated with face-recognition ability, evaluated from two independent measures. We also showed that the abilities of developmental prosopagnosics and super-recognizers are explained by a model that predicts face-recognition ability from the use of information built solely from participants with intermediate face-recognition abilities ( n = 99). This supports the hypothesis that the use of information varies quantitatively from developmental prosopagnosics to super-recognizers as a function of face-recognition ability.

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Shakeshaft, Nicholas G., and Robert Plomin. "Genetic specificity of face recognition." Proceedings of the National Academy of Sciences 112, no. 41 (September 28, 2015): 12887–92. http://dx.doi.org/10.1073/pnas.1421881112.

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Specific cognitive abilities in diverse domains are typically found to be highly heritable and substantially correlated with general cognitive ability (g), both phenotypically and genetically. Recent twin studies have found the ability to memorize and recognize faces to be an exception, being similarly heritable but phenotypically substantially uncorrelated both with g and with general object recognition. However, the genetic relationships between face recognition and other abilities (the extent to which they share a common genetic etiology) cannot be determined from phenotypic associations. In this, to our knowledge, first study of the genetic associations between face recognition and other domains, 2,000 18- and 19-year-old United Kingdom twins completed tests assessing their face recognition, object recognition, and general cognitive abilities. Results confirmed the substantial heritability of face recognition (61%), and multivariate genetic analyses found that most of this genetic influence is unique and not shared with other cognitive abilities.

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Russell, Richard, Brad Duchaine, and Ken Nakayama. "Super-recognizers: People with extraordinary face recognition ability." Psychonomic Bulletin & Review 16, no. 2 (April 2009): 252–57. http://dx.doi.org/10.3758/pbr.16.2.252.

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Furl, Nicholas, Lúcia Garrido, Raymond J. Dolan, Jon Driver, and Bradley Duchaine. "Fusiform Gyrus Face Selectivity Relates to Individual Differences in Facial Recognition Ability." Journal of Cognitive Neuroscience 23, no. 7 (July 2011): 1723–40. http://dx.doi.org/10.1162/jocn.2010.21545.

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Regions of the occipital and temporal lobes, including a region in the fusiform gyrus (FG), have been proposed to constitute a “core” visual representation system for faces, in part because they show face selectivity and face repetition suppression. But recent fMRI studies of developmental prosopagnosics (DPs) raise questions about whether these measures relate to face processing skills. Although DPs manifest deficient face processing, most studies to date have not shown unequivocal reductions of functional responses in the proposed core regions. We scanned 15 DPs and 15 non-DP control participants with fMRI while employing factor analysis to derive behavioral components related to face identification or other processes. Repetition suppression specific to facial identities in FG or to expression in FG and STS did not show compelling relationships with face identification ability. However, we identified robust relationships between face selectivity and face identification ability in FG across our sample for several convergent measures, including voxel-wise statistical parametric mapping, peak face selectivity in individually defined “fusiform face areas” (FFAs), and anatomical extents (cluster sizes) of those FFAs. None of these measures showed associations with behavioral expression or object recognition ability. As a group, DPs had reduced face-selective responses in bilateral FFA when compared with non-DPs. Individual DPs were also more likely than non-DPs to lack expected face-selective activity in core regions. These findings associate individual differences in face processing ability with selectivity in core face processing regions. This confirms that face selectivity can provide a valid marker for neural mechanisms that contribute to face identification ability.

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Wimbarti, Supra, and Willy Kristianto Yappy. "Development of Face Recognition Software to Differentiate Autism Spectrum Disorder from Typical Adolescents." Jurnal Psikologi 47, no. 3 (December 23, 2020): 163. http://dx.doi.org/10.22146/jpsi.60459.

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The purpose of this research was two folds: (1) to establish the face recognition software; and (2) to differentiate the ability in face recognition between Autism Spectrum Disorder (ASD) from typical adolescents. The subjects were adolescents between 10-16 years old. The experimental group consisted of 31 adolescents with ASD, the control group consisted of 32 typical adolescents. Research was done using experimental method, with face recognition software. Data collected were the number of correct faces recognized and the time needed to recognize and touch the face. The hypothesis was adolescents with ASD have worse face recognition ability compared to typical adolescents. Result showed that there were differences between ASD group compared to normal group both in the number of correct face recognitions and the time needed to choose a face with a finger touch. A one-way MANOVA revealed a significant multivariate main effect for type of groups toward the number of correct answers and time needed to answer, Wilks’ λ = 0.739, F (2,60) = 10.610, p < 0.001. The univariate main effect were also examined, whereupon significant univariate main effect for type of groups were obtained for both the amount of correct answer, F (1,61) = 15.468 p < 0.001, and the amount of time needed to answer, F (1,61) = 21.360 p < 0.001.

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Homorogan, C., R. Adam, R. Barboianu, Z. Popovici, C. Bredicean, and M. Ienciu. "Emotional Face Recognition in Bipolar Disorder." European Psychiatry 41, S1 (April 2017): S117. http://dx.doi.org/10.1016/j.eurpsy.2017.01.1904.

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IntroductionEmotional face recognition is significant for social communication. This is impaired in mood disorders, such as bipolar disorder. Individuals with bipolar disorder lack the ability to perceive facial expressions.ObjectivesTo analyse the capacity of emotional face recognition in subjects diagnosed with bipolar disorder.AimsTo establish a correlation between emotion recognition ability and the evolution of bipolar disease.MethodsA sample of 24 subjects were analysed in this trial, diagnosed with bipolar disorder (according to ICD-10 criteria), who were hospitalised in the Psychiatry Clinic of Timisoara and monitored in outpatients clinic. Subjects were introduced in the trial based on inclusion/exclusion criteria. The analysed parameters were: socio-demographic (age, gender, education level), the number of relapses, the predominance of manic or depressive episodes, and the ability of identifying emotions (Reading the Mind in the Eyes Test).ResultsMost of the subjects (79.16%) had a low ability to identify emotions, 20.83% had a normal capacity to recognise emotions, and none of them had a high emotion recognition capacity. The positive emotions (love, joy, surprise) were easier recognised, by 75% of the subjects, than the negative ones (anger, sadness, fear). There was no evident difference in emotional face recognition between the individuals with predominance of manic episodes than the ones who had mostly depressive episodes, and between the number of relapses.ConclusionsThe individuals with bipolar disorder have difficulties in identifying facial emotions, but with no obvious correlation between the analysed parameters.Disclosure of interestThe authors have not supplied their declaration of competing interest.

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Wilmer, J. B., L. Germine, C. F. Chabris, G. Chatterjee, M. Williams, E. Loken, K. Nakayama, and B. Duchaine. "Human face recognition ability is specific and highly heritable." Proceedings of the National Academy of Sciences 107, no. 11 (February 22, 2010): 5238–41. http://dx.doi.org/10.1073/pnas.0913053107.

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Wang, Ruosi, Jingguang Li, Huizhen Fang, Moqian Tian, and Jia Liu. "Individual Differences in Holistic Processing Predict Face Recognition Ability." Psychological Science 23, no. 2 (January 5, 2012): 169–77. http://dx.doi.org/10.1177/0956797611420575.

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Turk, Matthew, and Alex Pentland. "Eigenfaces for Recognition." Journal of Cognitive Neuroscience 3, no. 1 (January 1991): 71–86. http://dx.doi.org/10.1162/jocn.1991.3.1.71.

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We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.

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Corrow, S., T. Donlon, J. Mathison, V. Adamson, and A. Yonas. "Differences in Face Recognition Ability Predicts Patterns of Holistic Face Processing in Children." Journal of Vision 14, no. 10 (August 22, 2014): 572. http://dx.doi.org/10.1167/14.10.572.

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DeGutis, Joseph, Xian Li, Bar Yosef, and Maruti V. Mishra. "Not so fast! Response times in the computerized Benton Face Recognition Test may not reflect face recognition ability." Cognitive Neuropsychology 39, no. 3-4 (May 19, 2022): 155–69. http://dx.doi.org/10.1080/02643294.2022.2114824.

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Laguesse, R., T. Tez, B. Hall, J. Irons, E. McKone, R. Daini, A. Albonico, et al. "Subjective self-assessment of face recognition ability is only weakly related to objective measures of face recognition performance." Journal of Vision 13, no. 9 (July 25, 2013): 979. http://dx.doi.org/10.1167/13.9.979.

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Wallace, Marcie A., and Martha J. Farah. "Savings in Relearning Face—Name Associations as Evidence for “Covert Recognition” in Prosopagnosia." Journal of Cognitive Neuroscience 4, no. 2 (April 1992): 150–54. http://dx.doi.org/10.1162/jocn.1992.4.2.150.

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Prosopagnosic patients appear to be impaired at recognizing faces. However, recent evidence for “covert recognition” in prosopagnosia has been taken to suggest that the impairment is not in face recognition per se, but rather in conscious access to face recognition. The most widely used test for covert recognition of faces in prosopagnosia is the face-name relearning task, in which some prosopagnosics have been found to learn correct names for previously familiar faces more easily than incorrect names. Although this phenomenon is consistent with face recognition operating normally but out of reach of conscious awareness, it may also be consistent with an impairment in face recognition per se. Perhaps savings in relearning is sufficiently sensitive to the residual information contained in degraded face representations that are not detectable by overt measures of recognition. If so, then we should expect to observe this same savings in relearning when overt recognition is obliterated for reasons other than brain damage. In the present study, we used forgetting of face-name associations in normal subjects as a way of degrading recognition ability. We found the same dissociation between overt recognition performance and savings in relearning as observed in prosopagnosic patients. This implies that the performance of prosopagnosic patients in these tasks does not demand explanation in terms other than an impairment in face recognition per se.

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Estudillo, Alejandro J. "Self-reported face recognition abilities for own and other-race faces." Journal of Criminal Psychology 11, no. 2 (April 6, 2021): 105–15. http://dx.doi.org/10.1108/jcp-06-2020-0025.

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Purpose The other-race effect shows that people are better recognizing faces from their own-race compared to other-race faces. This effect can have dramatic consequences in applied scenarios whereby face identification is paramount, such as eyewitness identification. This paper aims to investigate whether observers have insights into their ability to recognize other-race faces. Design/methodology/approach Chinese ethnic observers performed objective measures of own- and other-race face recognition – the Cambridge Face Memory Test Chinese and the Cambridge Face Memory Test original; the PI20 – a 20-items self-reported measured of general face recognition abilities; and the ORE20 – a new developed 20-items self-reported measure of other-race face recognition. Findings Recognition of own-race faces was better compared to other-race faces. This effect was also evident at a phenomenological level, as observers reported to be worse recognizing other-race faces compared to own-race faces. Additionally, although a moderate correlation was found between own-race face recognition abilities and the PI20, individual differences in the recognition of other-race faces was only poorly associated with observers’ scores in the ORE20. Research limitations/implications These results suggest that observers’ insights to recognize faces are more consistent and reliable for own-race faces. Practical implications Self-reported measures of other-race recognition could produce misleading results. Thus, when evaluating eyewitness’ accuracy identifying other-race faces, objective measures should be used. Originality/value In contrast to own race recognition, people have very limited insights into their recognition abilities for other race faces.

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M H, Assyakirin, Shafriza Nisha B, Haniza Y, Fathinul Syahir A S, and Muhammad Juhairi A S. "Modelling of Facial Images for Analysis of Recognition System." Journal of Physics: Conference Series 2107, no. 1 (November 1, 2021): 012041. http://dx.doi.org/10.1088/1742-6596/2107/1/012041.

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Abstract Face recognition is categorized as a biometric technology that employs the use of computer ability in image processing to detect and recognize human faces. Face recognition system has numerous applications for many purposes such as for access control, law enforcement and surveillance thus this system is dominant in present technology. Generally, face recognition system become more advance in term of the accuracy and implementation. However, there are a few parameters that effects the accuracy of recognition system for examples, the pose invariant, illumination effect, size of image and noise tolerance. Even though there are a number of systems were already available in the literature, the complete understanding of their performances are relatively limited. This is due to many systems focused on a narrow application band – therefore, a comprehensive analysis are needed in order to understand their performances leading to establishing the conditions for successful face recognition system. In this paper we developed a synthetic model to represent facial images to be used as a platform for performance analysis of facial recognition systems. The model includes 5 face types with the ability to vary all parameters that are affecting recognition performance – measurement noise, face size and face-background intensity differences. The model is important as it provide an avenue for performance analysis of facial recognition systems.

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Wilmer, J. B., L. Germine, E. Loken, X. M. Guo, G. Chatterjee, K. Nakayama, M. Williams, C. F. Chabris, and B. Duchaine. "Response to Thomas: Is human face recognition ability entirely genetic?" Proceedings of the National Academy of Sciences 107, no. 24 (June 7, 2010): E101. http://dx.doi.org/10.1073/pnas.1004299107.

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Ernest, Carole H. "Spatial ability and laterality effects on a face recognition task." Personality and Individual Differences 23, no. 5 (November 1997): 839–48. http://dx.doi.org/10.1016/s0191-8869(97)00076-7.

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Meyer, Kristina, Werner Sommer, and Andrea Hildebrandt. "Reflections and New Perspectives on Face Cognition as a Specific Socio-Cognitive Ability." Journal of Intelligence 9, no. 2 (June 11, 2021): 30. http://dx.doi.org/10.3390/jintelligence9020030.

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The study of socio-cognitive abilities emerged from intelligence research, and their specificity remains controversial until today. In recent years, the psychometric structure of face cognition (FC)—a basic facet of socio-cognitive abilities—was extensively studied. In this review, we summarize and discuss the divergent psychometric structures of FC in easy and difficult tasks. While accuracy in difficult tasks was consistently shown to be face-specific, the evidence for easy tasks was inconsistent. The structure of response speed in easy tasks was mostly—but not always—unitary across object categories, including faces. Here, we compare studies to identify characteristics leading to face specificity in easy tasks. The following pattern emerges: in easy tasks, face specificity is found when modeling speed in a single task; however, when modeling speed across multiple, different easy tasks, only a unitary factor structure is reported. In difficult tasks, however, face specificity occurs in both single task approaches and task batteries. This suggests different cognitive mechanisms behind face specificity in easy and difficult tasks. In easy tasks, face specificity relies on isolated cognitive sub-processes such as face identity recognition. In difficult tasks, face-specific and task-independent cognitive processes are employed. We propose a descriptive model and argue for FC to be integrated into common taxonomies of intelligence.

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Elbich, Daniel, and Suzy Scherf. "Over-Connectivity in the Face-Processing Network is Related to Weaker Face Recognition Ability." Journal of Vision 15, no. 12 (September 1, 2015): 166. http://dx.doi.org/10.1167/15.12.166.

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Gramigna, Remo, and Cristina Voto. "Notes on the semiotics of face recognition." Sign Systems Studies 49, no. 3-4 (December 31, 2021): 338–60. http://dx.doi.org/10.12697/sss.2021.49.3-4.05.

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Perceiving and recognizing others via their faces is of pivotal importance. The ability to perceive others in the environment – to discern between friends and foes, selves and others – as well as to detect and seek to predict their possible moves, plans, and intentions, is a set of skills that has proved to be essential in the evolutionary history of humankind. The aim of this study is to explore the subject of face recognition as a semiotic phenomenon. The scope of this inquiry is limited to face perception by the human species. The human face is analysed on the threshold between biological processes and cultural processes. We argue that the recognition of likenesses has a socio-cultural dimension that should not be overlooked. By drawing on Georg Lichtenberg’s remarks on physiognomy, we discuss the critique of the semiotic bias, the association of ideas, and the mechanism of typification involved in face recognition. Face typification is discussed against the background of face recognition and face identification. We take them as three gradients of meaning that map out a network of relationships concerning different cognitive operations that are at stake when dealing with the recognition of faces.

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Hosokawa, Hiroaki, Shigenori Kanno, Yoshiyuki Nishio, Iori Kawasaki, Kazumi Hirayama, Atsuko Sunaga, Naotake Shoji, et al. "Facial memory ability and self-awareness in patients with temporal lobe epilepsy after anterior temporal lobectomy." PLOS ONE 16, no. 4 (April 1, 2021): e0248785. http://dx.doi.org/10.1371/journal.pone.0248785.

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Anterior temporal lobectomy (ATL) is the most common surgical treatment for drug-resistant temporal lobe epilepsy (TLE). Right ATL has been reported to reduce facial memory ability in patients with TLE, as indicated by poor performance on the Warrington Recognition Memory Test for Faces (RMF), which is commonly used to evaluate visual memory in these patients. However, little is known about whether patients with TLE exhibit difficulties in identifying faces in daily life after ATL. The aim of this study was to investigate facial memory ability and self-awareness of face identification difficulties in patients with TLE after ATL. Sixteen patients with TLE after right ATL, 14 patients with TLE after left ATL, and 29 healthy controls were enrolled in this study. We developed the multiview face recognition test (MFRT), which comprises a learning phase (one or three frontal face images without external facial feature information) and a recognition phase (frontal, oblique, or noise-masked face images). Facial memory abilities were examined in all participants using the MFRT and RMF, and self-awareness of difficulties in face identification was evaluated using the 20-item prosopagnosia index (PI20), which has been widely used to assess developmental prosopagnosia. The MFRT performance in patients with TLE after ATL was significantly worse than that in healthy controls regardless of the resected side, whereas the RMF scores in patients with TLE were significantly worse than those in healthy controls only after right ATL. The MFRT performance in patients with TLE after both left and right ATL was more influenced by working memory load than that in healthy controls. The PI20 scores revealed that patients with TLE after left ATL were aware of their difficulties in identifying faces. These findings suggest that patients with TLE not only after right ATL but also after left ATL might have difficulties in face identification.

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., Nitin. "Smart Attendance Management System using Face Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3007–11. http://dx.doi.org/10.22214/ijraset.2021.35597.

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Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. In human interactions, the face is the most important factor as it contains important information about a person or individual. All humans have the ability to recognise individuals from their faces. Now following system is based on face recognition to maintain the attendance record of students. The daily attendance of students is recorded subject wise which is stored already by the administrator. As the time for corresponding subject arrives the system automatically starts taking snaps and then apply face detection and recognition technique to the given image and the recognize students are marked as present and their attendance update with corresponding time and subject id. We have used deep learning techniques to develop this system, histogram of oriented gradient method is used to detect faces in images and deep learning method is used to compute and compare facial feature of students to recognize them.

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Kadhim, Ansam, and Salah Al-Darraji. "Face Recognition System Against Adversarial Attack Using Convolutional Neural Network." Iraqi Journal for Electrical and Electronic Engineering 18, no. 1 (November 6, 2021): 1–8. http://dx.doi.org/10.37917/ijeee.18.1.1.

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Face recognition is the technology that verifies or recognizes faces from images, videos, or real-time streams. It can be used in security or employee attendance systems. Face recognition systems may encounter some attacks that reduce their ability to recognize faces properly. So, many noisy images mixed with original ones lead to confusion in the results. Various attacks that exploit this weakness affect the face recognition systems such as Fast Gradient Sign Method (FGSM), Deep Fool, and Projected Gradient Descent (PGD). This paper proposes a method to protect the face recognition system against these attacks by distorting images through different attacks, then training the recognition deep network model, specifically Convolutional Neural Network (CNN), using the original and distorted images. Diverse experiments have been conducted using combinations of original and distorted images to test the effectiveness of the system. The system showed an accuracy of 93% using FGSM attack, 97% using deep fool, and 95% using PGD.

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Ryer, David M., Trevor J. Bihl, Kenneth W. Bauer, and Steven K. Rogers. "QUEST Hierarchy for Hyperspectral Face Recognition." Advances in Artificial Intelligence 2012 (May 8, 2012): 1–13. http://dx.doi.org/10.1155/2012/203670.

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A qualia exploitation of sensor technology (QUEST) motivated architecture using algorithm fusion and adaptive feedback loops for face recognition for hyperspectral imagery (HSI) is presented. QUEST seeks to develop a general purpose computational intelligence system that captures the beneficial engineering aspects of qualia-based solutions. Qualia-based approaches are constructed from subjective representations and have the ability to detect, distinguish, and characterize entities in the environment Adaptive feedback loops are implemented that enhance performance by reducing candidate subjects in the gallery and by injecting additional probe images during the matching process. The architecture presented provides a framework for exploring more advanced integration strategies beyond those presented. Algorithmic results and performance improvements are presented as spatial, spectral, and temporal effects are utilized; additionally, a Matlab-based graphical user interface (GUI) is developed to aid processing, track performance, and to display results.

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Tree, Jeremy J., Ruth Horry, Howard Riley, and Jeremy B. Wilmer. "Are portrait artists superior face recognizers? Limited impact of adult experience on face recognition ability." Journal of Experimental Psychology: Human Perception and Performance 43, no. 4 (April 2017): 667–76. http://dx.doi.org/10.1037/xhp0000328.

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DeGutis, Joseph, Jeremy Wilmer, Rogelio J. Mercado, and Sarah Cohan. "Using regression to measure holistic face processing reveals a strong link with face recognition ability." Cognition 126, no. 1 (January 2013): 87–100. http://dx.doi.org/10.1016/j.cognition.2012.09.004.

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Agbolade, Olalekan, Azree Nazri, Razali Yaakob, Abdul Azim Ghani, and Yoke Kqueen Cheah. "Down Syndrome Face Recognition: A Review." Symmetry 12, no. 7 (July 17, 2020): 1182. http://dx.doi.org/10.3390/sym12071182.

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One of the most pertinent applications of image analysis is face recognition and one of the most common genetic disorders is Down syndrome (DS), which is caused by chromosome abnormalities in humans. It is currently a challenge in computer vision in the domain of DS face recognition to build an automated system that equals the human ability to recognize face as one of the symmetrical structures in the body. Consequently, the use of machine learning methods has facilitated the recognition of facial dysmorphic features associated with DS. This paper aims to present a concise review of DS face recognition using the currently published literature by following the generic face recognition pipeline (face detection, feature extraction, and classification) and to identify critical knowledge gaps and directions for future research. The technologies underlying facial analysis presented in recent studies have helped expert clinicians in general genetic disorders and DS prediction.

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Sunday, Mackenzie A., Parth A. Patel, Michael D. Dodd, and Isabel Gauthier. "Gender and hometown population density interact to predict face recognition ability." Vision Research 163 (October 2019): 14–23. http://dx.doi.org/10.1016/j.visres.2019.08.006.

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Bate, Sarah, Benjamin Parris, Catherine Haslam, and Janice Kay. "Socio-emotional functioning and face recognition ability in the normal population." Personality and Individual Differences 48, no. 2 (January 2010): 239–42. http://dx.doi.org/10.1016/j.paid.2009.10.005.

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Turano, Maria Teresa, and Maria Pia Viggiano. "The relationship between face recognition ability and socioemotional functioning throughout adulthood." Aging, Neuropsychology, and Cognition 24, no. 6 (October 18, 2016): 613–30. http://dx.doi.org/10.1080/13825585.2016.1244247.

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Davis, Josh P., Karen Lander, Ray Evans, and Ashok Jansari. "Investigating Predictors of Superior Face Recognition Ability in Police Super-recognisers." Applied Cognitive Psychology 30, no. 6 (August 2, 2016): 827–40. http://dx.doi.org/10.1002/acp.3260.

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Royer, Jessica, Caroline Blais, Isabelle Charbonneau, Karine Déry, Jessica Tardif, Brad Duchaine, Frédéric Gosselin, and Daniel Fiset. "Greater reliance on the eye region predicts better face recognition ability." Cognition 181 (December 2018): 12–20. http://dx.doi.org/10.1016/j.cognition.2018.08.004.

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Grundmann, Felix, Kai Epstude, and Susanne Scheibe. "Face masks reduce emotion-recognition accuracy and perceived closeness." PLOS ONE 16, no. 4 (April 23, 2021): e0249792. http://dx.doi.org/10.1371/journal.pone.0249792.

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Face masks became the symbol of the global fight against the coronavirus. While face masks’ medical benefits are clear, little is known about their psychological consequences. Drawing on theories of the social functions of emotions and rapid trait impressions, we tested hypotheses on face masks’ effects on emotion-recognition accuracy and social judgments (perceived trustworthiness, likability, and closeness). Our preregistered study with 191 German adults revealed that face masks diminish people’s ability to accurately categorize an emotion expression and make target persons appear less close. Exploratory analyses further revealed that face masks buffered the negative effect of negative (vs. non-negative) emotion expressions on perceptions of trustworthiness, likability, and closeness. Associating face masks with the coronavirus’ dangers predicted higher perceptions of closeness for masked but not for unmasked faces. By highlighting face masks’ effects on social functioning, our findings inform policymaking and point at contexts where alternatives to face masks are needed.

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Meinhardt, G., B. Meinhardt-Injac, and M. Persike. "Orientation-invariance of individual differences in three face processing tasks." Royal Society Open Science 6, no. 1 (January 2019): 181350. http://dx.doi.org/10.1098/rsos.181350.

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Numerous studies have reported impairments in perception and recognition, and, particularly, in part-integration of faces following picture-plane inversion. Whether these findings support the notion that inversion changes face processing qualitatively remains a topic of debate. To examine whether associations and dissociations of the human face processing ability depend on stimulus orientation, we measured face recognition with the Cambridge Face Memory Test (CFMT), along with experimental tests of face perception and selective attention to faces and non-face objects in a sample of 314 participants. Results showed strong inversion effects for all face-related tasks, and modest ones for non-face objects. Individual differences analysis revealed that the CFMT shared common variance with face perception and face-selective attention, however, independent of orientation. Regardless of whether predictor and criterion had same or different orientation, face recognition was best predicted by the same test battery. Principal component decomposition revealed a common factor for face recognition and face perception, a second common factor for face recognition and face-selective attention, and two unique factors. The patterns of factor loadings were nearly identical for upright and inverted presentation. These results indicate orientation-invariance of common variance in three domains of face processing. Since inversion impaired performance, but did not affect domain-related associations and dissociations, the findings suggest process-specific but orientation-general mechanisms. Specific limitations by constraints of individual differences analysis and test selection are discussed.

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Royer, Jessica, Caroline Blais, Frédéric Gosselin, Justin Duncan, and Daniel Fiset. "When less is more: Impact of face processing ability on recognition of visually degraded faces." Journal of Experimental Psychology: Human Perception and Performance 41, no. 5 (2015): 1179–83. http://dx.doi.org/10.1037/xhp0000095.

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Marcus, David J., and Charles A. Nelson. "Neural Bases and Development of Face Recognition in Autism." CNS Spectrums 6, no. 1 (January 2001): 36–44. http://dx.doi.org/10.1017/s1092852900022872.

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AbstractThis paper critically examines the literature on face recognition in autism, including a discussion of the neural correlates of this ability. The authors begin by selectively reviewing the behavioral and cognitive neuroscience research on whether faces are represented by a “special” behavioral and neural system—one distinct from object processing. The authors then offer a neuroconstructivist model that attempts to account for the robust finding that certain regions in the inferior temporal cortex are recruited in the service of face recognition. This is followed by a review of the evidence supporting the view that face recognition is atypical in individuals with autism. This face-recognition deficit may indicate a continued risk for the further development of social impairments. The authors conclude by speculating on the role of experience in contributing to this atypical developmental pattern and its implications for normal development of face processing.

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KHALAJZADEH, HURIEH, MOHAMMAD MANSOURI, and MOHAMMAD TESHNEHLAB. "HIERARCHICAL STRUCTURE BASED CONVOLUTIONAL NEURAL NETWORK FOR FACE RECOGNITION." International Journal of Computational Intelligence and Applications 12, no. 03 (September 2013): 1350018. http://dx.doi.org/10.1142/s1469026813500181.

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In this paper, a hierarchical structure based convolutional neural network is proposed to provide the ability for robust information processing. The weight sharing ability of convolutional neural networks (CNNs) is considered as a level of hierarchy in these networks. Weight sharing reduces the number of free parameters and improves the generalization ability. In the proposed structure, a small CNN which is used for feature extractor is shared between the whole input image pixels. A scalable architecture for implementing extensive CNNs is resulted using a smaller and modularized trainable network to solve a large and complicated task. The proposed structure causes less training time, fewer numbers of parameters and higher test data accuracy. The recognition accuracy for recognizing unseen data shows improvement in generalization. Also presented are application examples for face recognition. The comprehensive experiments completed on ORL, Yale and JAFFE face databases show improved classification rates and reduced training time and network parameters.

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Gross, Cornelia, and Gudrun Schwarzer. "Face recognition across varying poses in 7- and 9-month-old infants: The role of facial expression." International Journal of Behavioral Development 34, no. 5 (June 3, 2010): 417–26. http://dx.doi.org/10.1177/0165025409350364.

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Three studies were conducted to determine whether 7- and 9-month-old infants generalize face identity to a novel pose of the same face when only internal face sections with and without an emotional expression were presented. In Study 1, 7- and 9-month-old infants were habituated to a full frontal or three-quarter pose of a face with neutral facial expression. In Study 2, 7-month-olds were habituated to a face with a positive or negative expression. In the novelty preference test, immediately following habituation, infants were shown a pair of faces: the habituation face in a novel pose and a novel face in the same pose. Generalization of facial identity was inferred from longer fixation time to the novel face. Whereas 7-month-old infants did not dishabituate to the novel face with neutral expression, 9-month-olds fixated longer on the novel face with neutral expression (Study 1). However, when faces displayed a positive or negative expression 7-month-olds also looked longer at the novel face, indicating generalization of the habituation face to a novel pose (Study 2). Study 3 showed that 7-montholds’ generalization ability in Study 2 cannot be explained by an inability to discriminate between the two poses of the habituation face. Results showed 9- but not 7-month-olds recognized neutral looking faces in a novel pose, and 7-month-olds’ face recognition ability was enhanced by emotional facial expression.

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Ashritha, Korukanti, Korukanti Ashritha, and Sridhar Bhukya. "Automated Attendance System Using Face Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2096–99. http://dx.doi.org/10.22214/ijraset.2022.44212.

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Abstract: In the recent time automated face recognition has become a trend and has been developed very much , this is mainly due to two reasons; first it is due to availability of modern technologies and second is due to the ability to save time using face recognition in the process of taking attendance of students. Its usage will grow vast in the future as it saves a lot of time. It consumes a lot of time to take attendance manually and few might also fake the attendance, in order to prevent time consumption and avoid faking the attendance face recognition is used to identify the person present in the class and mark his attendance , this is done with the help of image or video frame. We proposed an automatic attendance management system using machine learning techniques such as CNN algorithm. The face detection and recognition will automatically detect the students in the classroom and mark the attendance by recognizing the person.. The faculty has access to add the student details such as name, USN, phone number, email-id. Then the image is captured through a high definition camera during the class hours. When the lecturing is going on faces of students are detected, segmented and stored for verification with database using the Convolutional Neural Networks (CNN) algorithm of machine learning technique

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Ruan, Shuai, Chaowei Tang, Xu Zhou, Zhuoyi Jin, Shiyu Chen, Haotian Wen, Hongbin Liu, and Dong Tang. "Multi-Pose Face Recognition Based on Deep Learning in Unconstrained Scene." Applied Sciences 10, no. 13 (July 7, 2020): 4669. http://dx.doi.org/10.3390/app10134669.

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At present, deep learning drives the rapid development of face recognition. However, in the unconstrained scenario, the change of facial posture has a great impact on face recognition. Moreover, the current model still has some shortcomings in accuracy and robustness. The existing research has formulated two methods to solve the above problems. One method is to model and train each pose separately. Then, a fusion decision will be made. The other method is to make “frontal” faces on the image or feature level and transform them into “frontal” face recognition. Based on the second idea, we propose a profile to the frontal revise mapping (PTFRM) module. This module realizes the revision of arbitrary poses on the feature level and transforms the multi-pose features into an approximate frontal representation to enhance the recognition ability of the existing recognition models. Finally, we evaluate the PTFRM on unconstrained face validation benchmark datasets such as Labeled Faces in the Wild (LFW), Celebrities in Frontal Profile (CFP), and IARPA Janus Benchmark A(IJB-A). Results show that the chosen method for this study achieves good performance.

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Li, Xiaopeng, Jinzhi Du, Jialin Yang, and Shuqin Li. "When Mobilenetv2 Meets Transformer: A Balanced Sheep Face Recognition Model." Agriculture 12, no. 8 (July 29, 2022): 1126. http://dx.doi.org/10.3390/agriculture12081126.

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Sheep face recognition models deployed on edge devices require a good trade-off between model size and accuracy, but the existing recognition models cannot do so. To solve the above problems, this paper combines Mobilenetv2 with Vision Transformer to propose a balanced sheep face recognition model called MobileViTFace. MobileViTFace enhances the model’s ability to extract fine-grained features and suppress the interference of background information through Transformer to distinguish different sheep faces more effectively. Thus, it can distinguish different sheep faces more effectively. The recognition accuracy of 96.94% is obtained on a self-built dataset containing 5490 sheep face photos of 105 sheep, which is a 9.79% improvement compared with MobilenetV2, with only a small increase in Params (the number of parameters) and FLOPs (floating-point operations). Compared to models such as Swin-small, which currently performs SOTA, Params and FLOPs are reduced by nearly ten times, whereas recognition accuracy is only 0.64% lower. Deploying MobileViTFace on the Jetson Nano-based edge computing platform, real-time and accurate recognition results are obtained, which has implications for practical production.

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Anilkumar, Anantha P. P., Veena Kumari, Ravi Mehrotra, Ingrid Aasen, Martina T. Mitterschiffthaler, and Tonmoy Sharma. "An fMRI study of face encoding and recognition in first-episode schizophrenia." Acta Neuropsychiatrica 20, no. 3 (June 2008): 129–38. http://dx.doi.org/10.1111/j.1601-5215.2008.00280.x.

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Background:Schizophrenia has been associated with limited abilities to interact effectively in social situations. Face perception and ability to recognise familiar faces are critical for social interaction. Patients with chronic schizophrenia are known to show impaired face recognition. Studying first-episode (FE) patients allows the exclusion of confounding effects of chronicity, medication and institutionalisation in this deficit.Objective:To determine brain (dys)functions during a face encoding and recognition paradigm in FE schizophrenia.Methods:Thirteen antipsychotic-naïve FE schizophrenia patients and 13 age- and sex-matched healthy controls underwent functional magnetic resonance imaging during a face encoding and recognition paradigm. Behavioural responses were recorded on line.Results:Patients recognised significantly fewer of previously presented faces than the controls (p = 0.008). At the neural level, both groups activated a network of regions including the fusiform area, occipital, temporal and frontal regions. In brain activity, the two groups did not differ in any region during encoding or recognition conditions (p > 0.05, corrected or uncorrected).Conclusions:Our findings show impaired face recognition without a significant alteration of related brain activity in FE schizophrenia patients. It is possible that neural changes become more strongly evident with progression of the illness, and manifest themselves as behavioural impairments during the early course.

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Ziccardi, Stefano, Francesco Crescenzo, and Massimiliano Calabrese. "“What Is Hidden behind the Mask?” Facial Emotion Recognition at the Time of COVID-19 Pandemic in Cognitively Normal Multiple Sclerosis Patients." Diagnostics 12, no. 1 (December 27, 2021): 47. http://dx.doi.org/10.3390/diagnostics12010047.

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Social cognition deficits have been described in people with multiple sclerosis (PwMS), even in absence of a global cognitive impairment, affecting predominantly the ability to adequately process emotions from human faces. The COVID-19 pandemic has forced people to wear face masks that might interfere with facial emotion recognition. Therefore, in the present study, we aimed at investigating the ability of emotion recognition in PwMS from faces wearing masks. We enrolled a total of 42 cognitively normal relapsing–remitting PwMS and a matched group of 20 healthy controls (HCs). Participants underwent a facial emotion recognition task in which they had to recognize from faces wearing or not surgical masks which of the six basic emotions (happiness, anger, fear, sadness, surprise, disgust) was presented. Results showed that face masks negatively affected emotion recognition in all participants (p < 0.001); in particular, PwMS showed a global worse accuracy than HCs (p = 0.005), mainly driven by the “no masked” (p = 0.021) than the “masked” (p = 0.064) condition. Considering individual emotions, PwMS showed a selective impairment in the recognition of fear, compared with HCs, in both the conditions investigated (“masked”: p = 0.023; “no masked”: p = 0.016). Face masks affected negatively also response times (p < 0.001); in particular, PwMS were globally hastier than HCs (p = 0.024), especially in the “masked” condition (p = 0.013). Furthermore, a detailed characterization of the performance of PwMS and HCs in terms of accuracy and response speed was proposed. Results from the present study showed the effect of face masks on the ability to process facial emotions in PwMS, compared with HCs. Healthcare professionals working with PwMS at the time of the COVID-19 outbreak should take into consideration this effect in their clinical practice. Implications in the everyday life of PwMS are also discussed.

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Borkar, Yasar, Reeve Mascarenhas, Shubham Tambadkar, and Jayanand P. Gawande. "Comparison of Real-Time Face Detection and Recognition Algorithms." ITM Web of Conferences 44 (2022): 03046. http://dx.doi.org/10.1051/itmconf/20224403046.

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With the phenomenal growth of video and image databases, there is a tremendous need for intelligent systems to automatically understand and examine information, as doing so manually is becoming increasingly difficult. The face is important in social interactions because it conveys information. Detecting a person's identity and feelings Humans do not have a great deal of ability to identify. Machines have different faces. As a result, an automatic face detection system is essential.in face recognition, facial expression recognition, head-pose estimation, and human–computer interaction, and so on Face detection is a computer technology that determines the location and size of a person's face. It also creates a digital image of a human face. Face detection has been a standout topic in the science field This paper provides an in-depth examination of the various techniques investigated for face detection in digital images. Various face challenges and applications. This paper also discusses detection. Detection features are also provided. In addition, we hold special discussions on the practical aspects of developing a robust face detection system, and finally. This paper concludes with several promising research directions for the future.

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Journal articles on the topic 'Face recognition ability'

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