Le déficit social, incluant la perturbation du traitement du regard et des émotions, est au cœur de l’autisme. Des études ont montré que les visages de peur provoquent une orientation rapide et involontaire de l’attention spatiale vers leur emplacement chez les individus à développement typique. De plus, ceux-ci détectent plus rapidement et plus efficacement les visages avec un regard direct (vs regard dévié). La présente étude vise à explorer l’effet de l’émotion de peur et de la direction du regard (direct vs dévié) sur l’attention spatiale chez les enfants autistes à l’aide d’une tâche d’attention spatiale implicite. Six enfants avec un trouble autistique (TA) ont participé à cette étude. Les participants doivent détecter l’apparition d’une cible à gauche ou à droite d’un écran. L’apparition de la cible est précédée d’une amorce (paire de visages peur/neutre avec regard direct/dévié). La cible peut être présentée soit dans le même champ visuel que l’amorce émotionnellement chargée (condition valide), soit dans le champ visuel opposé (condition invalide). Nos résultats montrent que les amorces avec un visage de peur (vs les amorces avec un visage neutre) provoquent un effet d’interférence au niveau comportemental et divergent l’attention de leur emplacement chez les enfants avec un TA. / Autism is characterized by a social deficit, including difficulties in using and responding to facial expressions and gaze. Previous studies showed that fearful faces elicit a rapid involuntary orienting of spatial attention towards their location in typically developing (TD) individuals. In addition, target faces with direct gaze are detected faster and more efficiently than those with averted gaze in TD individuals. The aim of the current study is to explore the effect of fear and gaze direction (direct vs averted) on spatial attention in children with autistic disorder (AD). Six children with AD performed a covert spatial orienting task. Each trial consisted of a pair of faces (fearful/neutral with direct/averted gaze) briefly presented followed by a target presented at the location of one of the faces. Participants had to judge the location of the target (right or left visual field). The target unpredictably appeared on the side of the emotional face (fear, direct) (valid condition) or on the opposite side (neutral, averted) (invalid condition). Our results show that fearful faces have an interferent effect on the performance of AD children and divert attention from their location.

Central to human intelligence, visual categorization is a skill that is both remarkably fast and accurate. Although there have been numerous studies in primates regarding how information flows in inferiortemporal (ITC) and prefrontal (PFC) cortices during online discrimination of visual categories, there has been little comparable research on the human cortex. To bridge this gap, this thesis explores how visual categories emerge in prefrontal cortex and the ventral stream, which is the human homologue of ITC. In particular, cortical spatiotemporal plasticity in visual category learning was investigated using behavioral experiments, magnetoencephalographic (MEG) imaging, and statistical machine learning methods. From a theoretical perspective, scientists from work on non-human primates have posited that PFC plays a primary role in the encoding of visual categories. Much of the extant research in the cognitive neuroscience literature, however, emphasizes the role of the ventral stream. Despite their apparent incompatibility, no study has evaluated these theories in the human cortex by examining the roles of the ventral stream and PFC in online discrimination and acquisition of visual categories. To address this question, I conducted two learning experiments using visually-similar categories as stimuli and recorded cortical response using MEG—a neuroimaging technique that offers a millisecond temporal resolution. Across both experiments, categorical information was found to be available during the period of cortical activity. Moreover, late in the learning process, this information is supplied increasingly in the ventral stream but less so in prefrontal cortex. These findings extend previous theories by suggesting that the ventral stream is crucial to long-term encoding of visual categories when categorical perception is proficient, but that PFC jointly encodes visual categories early on during learning. From a methodological perspective, MEG is limited as a technique because it can lead to false discoveries in a large number of spatiotemporal regions of interest (ROIs) and, typically, can only coarsely reconstruct the spatial locations of cortical responses. To address the first problem, I developed an excursion algorithm that identified ROIs contiguous in time and space. I then used a permutation test to measure the global statistical significance of the ROIs. To address the second problem, I developed a method that incorporates domainspecific and experimental knowledge in the modeling process. Utilizing faces as a model category, I used a predefined “face” network to constrain the estimation of cortical activities by applying differential shrinkages to regions within and outside this network. I proposed and implemented a trial-partitioning approach which uses trials in the midst of learning for model estimation. Importantly, this renders localizing trials more precise in both the initial and final phases of learning. In summary, this thesis makes two significant contributions. First, it methodologically improves the way we can characterize the spatiotemporal properties of the human cortex using MEG. Second, it provides a combined theory of visual category learning by incorporating the large time scales that encompass the course of the learning.

The central aim of the current thesis was to examine how facial expression and racial identity information affect face processing involving different races, and this was addressed by studying several types of face processing tasks including face recognition, emotion perception/recognition, face perception and attention to faces. In particular, the effect of facial expression on the differential processing of own and other race faces (the so-called the own-race bias) was examined from two perspectives, examining the effect both at the level of perceptual expertise favouring the processing of own-race faces and in-group bias influencing face processing in terms of a self-enhancing dimension. Results from the face recognition study indicated a possible similarity between familiar/unfamiliar and own-race/other-race face processing. Studies on facial expression perception and memory showed that there was no indication of in-group bias in face perception and memory, although a common finding throughout was that different race faces were often associated with different types of facial expressions. The most consistent finding across all studies was that the effect of the own-race bias was more evident amongst European participants. Finally, results from the face attention study showed that there were no signs of preferential visual attention to own-race faces. The results from the current research provided further evidence to the growing body of knowledge regarding the effects of the own-race bias. Based on this knowledge, for future studies it is suggested that a better understanding of the mechanisms underlying the own-race bias would help advance this interesting and ever-evolving area of research further.

CAPES / O reconhecimento facial está se tornando uma tarefa comum com a evolução da tecnologia da informação. Este artefato pode ser utilizado na área de segurança, controlando acesso a lugares restritos, identificando pessoas que tenham cometido atos ilícitos, entre outros. Executar o reconhecimento facial é uma tarefa complexa e, para completar este processo, são implementadas etapas que compreendem: a captura de imagens faciais, a detecção de regiões de interesse, a normalização facial, a extração de características e o reconhecimento em si. Dentre estas, as três primeiras são tratadas neste trabalho, que tem como objetivo principal a normalização automática de faces. Tanto para a captura de imagens quanto para a normalização frontal existem normas internacionais que padronizam o procedimento de execução destas tarefas e que foram utilizadas neste trabalho. Além disto, algumas normas foram adaptadas para a construção de uma base de imagens faciais com o objetivo de auxiliar o processo de reconhecimento facial. Também foi criada uma nova metodologia para normalização de imagens faciais laterais, baseando-se nas normas da normalização frontal. Foram implementadas normalização semiautomática frontal, semiautomática lateral e automática lateral. Para a execução da normalização facial automática são necessários dois pontos de controle, os dois olhos, o que torna indispensável a execução da etapa de detecção de regiões de interesse. Neste trabalho, foram comparadas duas metodologias semelhantes para detecção. Primeiramente foi detectada uma região contendo ambos os olhos e, em seguida, dentro desta região, foram detectados cada um dos olhos de forma mais precisa. Para as duas metodologias foram utilizadas técnicas de processamento de imagens e reconhecimento de padrões. A primeira metodologia utiliza como filtro o Haar-Like Features em conjunto com a técnica de reconhecimento de padrões Adaptative Boosting. Sendo que as técnicas equivalentes no segundo algoritmo foram o Local Binary Pattern e o Support Vector Machines, respectivamente. Na segunda metodologia também foi utilizado um algoritmo de otimização de busca baseado em vizinhança, o Variable Neighborhood Search. Os estudos resultaram em uma base com 3726 imagens, mais uma base normalizada frontal com 966 imagens e uma normalizada lateral com 276 imagens. A detecção de olhos resultou, nos melhores testes, em aproximadamente 99% de precisão para a primeira metodologia e 95% para a segunda, sendo que em todos os testes a primeira foi o mais rápida. Com o desenvolvimento de trabalhos futuros pretende-se: tornar públicas as bases de imagens, melhorar a porcentagem de acerto e velocidade de processamento para todos os testes e melhorar a normalização, implementando a normalização de plano de fundo e também de iluminação. / With the evolution of information technology Facial recognition is becoming a common task. This artifact can be used in security, controlling access to restricted places and identifying persons, for example. Facial recognition is a complex task, and it's divided into some process, comprising: facial images capture, detection of regions of interest, facial normalization, feature extraction and recognition itself. Among these, the first three are treated in this work, which has as its main objective the automatic normalization of faces. For the capture of images and for the image normalization there are international standards that standardize the procedure for implementing these tasks and which were used in this work. In addition to following these rules, other standardizations have been developed to build a database of facial images in order to assist the process of face recognition. A new methodology for normalization of profile faces, based on the rules of frontal normalization. Some ways of normalization were implemented: frontal semiautomatic, lateral semiautomatic and automatic frontal. For the execution of frontal automatic normalization we need two points of interest, the two eyes, which makes it a necessary step to execute the detection regions of interest. In this work, we compared two similar methods for detecting. Where was first detected a region containing both eyes and then, within this region were detected each eye more accurately. For the two methodologies were used techniques of image processing and pattern recognition. The first method based on the Viola and Jones algorithm, the filter uses as Haar-like Features with the technique of pattern recognition Adaptive Boosting. Where the second algorithm equivalent techniques were Local Binary Pattern and Support Vector Machines, respectively. In the second algorithm was also used an optimization algorithm based on neighborhood search, the Variable Neighborhood Search. This studies resulted in a database with 3726 images, a frontal normalized database with 966 images and a database with face's profile normalized with 276 images. The eye detection resulted in better tests, about 99 % accuracy for the first method and 95 % for the second, and in all tests the first algorithm was the fastest. With the development of future work we have: make public the images database, improve the percentage of accuracy and processing speed for all tests and improve the normalization by implementing the normalization of the background and also lighting.

CAPES / O reconhecimento facial está se tornando uma tarefa comum com a evolução da tecnologia da informação. Este artefato pode ser utilizado na área de segurança, controlando acesso a lugares restritos, identificando pessoas que tenham cometido atos ilícitos, entre outros. Executar o reconhecimento facial é uma tarefa complexa e, para completar este processo, são implementadas etapas que compreendem: a captura de imagens faciais, a detecção de regiões de interesse, a normalização facial, a extração de características e o reconhecimento em si. Dentre estas, as três primeiras são tratadas neste trabalho, que tem como objetivo principal a normalização automática de faces. Tanto para a captura de imagens quanto para a normalização frontal existem normas internacionais que padronizam o procedimento de execução destas tarefas e que foram utilizadas neste trabalho. Além disto, algumas normas foram adaptadas para a construção de uma base de imagens faciais com o objetivo de auxiliar o processo de reconhecimento facial. Também foi criada uma nova metodologia para normalização de imagens faciais laterais, baseando-se nas normas da normalização frontal. Foram implementadas normalização semiautomática frontal, semiautomática lateral e automática lateral. Para a execução da normalização facial automática são necessários dois pontos de controle, os dois olhos, o que torna indispensável a execução da etapa de detecção de regiões de interesse. Neste trabalho, foram comparadas duas metodologias semelhantes para detecção. Primeiramente foi detectada uma região contendo ambos os olhos e, em seguida, dentro desta região, foram detectados cada um dos olhos de forma mais precisa. Para as duas metodologias foram utilizadas técnicas de processamento de imagens e reconhecimento de padrões. A primeira metodologia utiliza como filtro o Haar-Like Features em conjunto com a técnica de reconhecimento de padrões Adaptative Boosting. Sendo que as técnicas equivalentes no segundo algoritmo foram o Local Binary Pattern e o Support Vector Machines, respectivamente. Na segunda metodologia também foi utilizado um algoritmo de otimização de busca baseado em vizinhança, o Variable Neighborhood Search. Os estudos resultaram em uma base com 3726 imagens, mais uma base normalizada frontal com 966 imagens e uma normalizada lateral com 276 imagens. A detecção de olhos resultou, nos melhores testes, em aproximadamente 99% de precisão para a primeira metodologia e 95% para a segunda, sendo que em todos os testes a primeira foi o mais rápida. Com o desenvolvimento de trabalhos futuros pretende-se: tornar públicas as bases de imagens, melhorar a porcentagem de acerto e velocidade de processamento para todos os testes e melhorar a normalização, implementando a normalização de plano de fundo e também de iluminação. / With the evolution of information technology Facial recognition is becoming a common task. This artifact can be used in security, controlling access to restricted places and identifying persons, for example. Facial recognition is a complex task, and it's divided into some process, comprising: facial images capture, detection of regions of interest, facial normalization, feature extraction and recognition itself. Among these, the first three are treated in this work, which has as its main objective the automatic normalization of faces. For the capture of images and for the image normalization there are international standards that standardize the procedure for implementing these tasks and which were used in this work. In addition to following these rules, other standardizations have been developed to build a database of facial images in order to assist the process of face recognition. A new methodology for normalization of profile faces, based on the rules of frontal normalization. Some ways of normalization were implemented: frontal semiautomatic, lateral semiautomatic and automatic frontal. For the execution of frontal automatic normalization we need two points of interest, the two eyes, which makes it a necessary step to execute the detection regions of interest. In this work, we compared two similar methods for detecting. Where was first detected a region containing both eyes and then, within this region were detected each eye more accurately. For the two methodologies were used techniques of image processing and pattern recognition. The first method based on the Viola and Jones algorithm, the filter uses as Haar-like Features with the technique of pattern recognition Adaptive Boosting. Where the second algorithm equivalent techniques were Local Binary Pattern and Support Vector Machines, respectively. In the second algorithm was also used an optimization algorithm based on neighborhood search, the Variable Neighborhood Search. This studies resulted in a database with 3726 images, a frontal normalized database with 966 images and a database with face's profile normalized with 276 images. The eye detection resulted in better tests, about 99 % accuracy for the first method and 95 % for the second, and in all tests the first algorithm was the fastest. With the development of future work we have: make public the images database, improve the percentage of accuracy and processing speed for all tests and improve the normalization by implementing the normalization of the background and also lighting.

The problem of face detection and recognition within a given database has become one of the important problems in computer vision. A simple approach for Face Detection in video is to run a learning based face detector every frame. But such an approach is computationally expensive and completely ignores the temporal continuity present in videos. Moreover the search space can be reduced by utilizing visual cues extracted based on the relevant task at hand(top down approach). Once detection is done next step is to perform a face recognition based on the available database. But the faces detected from face detect or output is neither aligned nor well cropped and is prone to scale change. We call such faces as free form faces. But the current existing algorithms on face recognition assume faces to be properly aligned and cropped, and having the same scale as the faces in the database, which is highly constrained. In this thesis, we propose an integrated detect-track framework for Multiview face detection in videos. We overcome the limitations of the frame based approaches, by utilizing the temporal continuity present in videos and also incorporating the top down information of the task. We model the problem based on the concept from Experiential sampling [2]. This consists of determining certain key positions which are relevant to the task(face detection). These key positions are referred to as attention samples and Multiview face detection is performed only at these locations. These statistical samples are estimated based on the visual cues, past experience and the temporal continuity and is modeled as a Bayesian filtering problem, which is solved using Particle Filters. In order to detect all views we use a tracker integrated with the detector and come out with a novel track termination algorithm using the concepts from Track Before Detect(TBD)[26]. Such an approach is computationally efficient and also results in lower false positive rate. We provide experiments showing the efficiency of the integrated detect-track approach over the multiview face detector approach without a tracker. For free form face recognition we propose to use the concept of Principal Geodesic Analysis(PGA) of the Covariance descriptors obtained from Gabor filters. This is similar to Principal Component Analysis in Euclidean spaces (Covariance descriptors lie on a Riemannian manifold). Such a descriptor is robust to alignment and scaling problems and also are of lower dimensions. We also employ sparse modeling technique for Face recognition task using these Covariance descriptor which are dimensionally reduced by transforming them on to a tangent space, which we call PGA feature. Further, we improve upon the recognition results of linear sparse modeling, by non-linear mapping of the PGA features by employing “Kernel Trick” for these sparse models. We show that the Kernelized sparse models using the PGA features are indeed very efficient for free form face recognition by testing on two standard databases namely AR and YaleB database.

Minority phenotype preference, or the negative frequency-dependent selection is a type of selection, where a feature is more preferred, the lower it's frequency is in the population. Even a weak effect in other-preference based sexual selection can result in a sustainable polymorphism. This study reviews the phenomenon in the context of human visual facial features. Common trends in attractiveness shape the morphology of the human face towards uniformity. Individual recognition however, as a condition for most social relations, uses the wast variability of different features. This variability could be formed and maintained by minority phenotype preference. In our study we tested the effect of minority phenotype preference in the selection for rare hair and eye colors. In 120 unique sets, each containing six photographs, we experimentally manipulated the frequency of each color type. These sets were then shown to 226 human raters. We tested whether the relative frequency of each color type affected the rating of individual stimuli. In hair color, significant effect of minority phenotype preference was detected when females rated the photographs of men. When males rated the photographs of females, the effect was significant in eye color only. Key words: face perception, sexual selection,...

This study examines spontanneous, laic personality ratings of face in comparison to following questionnaires: 16 PF, EPQ-R, NEO-PI-R. We've focused on how people spontaneously rate personality from face in relation to traits which are examined by said questionnaires. We tried to discover which traits are part of questionnaires but not included qualiative ratings and vice versa which traits can be found in laic descriptions but not in questionnaires. We used data from previous studies which provided us with two independent data, self-reports and ratings of facial photographs. Ratings were sorted out and compared to questionnaires. Most of the traits from qualiative data were also included in NEO-PI-R, specifically 90,4% of traits from self-reports and 82,90% of peer reports. 16 PF covered 88% and 77,81% of traits, EPQ-R 86% and 75,94% respectively. The least covered factor overall was Self-Reliance (16 PF) which included only 0,4% of all traits from self-reports. We have not found any factor which would be completely neglected in qualiatitve descriptions. Atractivity and physical traits were one of the main traits among those left unclassified. As with atractivity itself there was wide range of traits that we are unsure of how much personality relevant they actually are. Among unclassified traits...

Il existe de grandes variations interindividuelles dans les habiletés pour la reconnaissance des visages. Alors que plusieurs avenues ont été explorées pour expliquer ces variations, leur source reste inconnue. L’utilisation d’information visuelle étant reliée à la performance pour n’importe quelle tâche, l’objectif du projet était d’utiliser la méthode des Bulles pour évaluer comment l’information visuelle utilisée est liée aux habiletés. Ainsi, les habiletés pour la reconnaissance des visages ont été mesurées chez 107 participants, un large échantillon d’individus normaux provenant du spectre complet d’habiletés, incluant les extrêmes de ce spectre (i.e. prosopagnosie développementale et super-reconnaissance). Ensuite, une tâche de reconnaissance de visages célèbres a été complétée, utilisant la méthode des Bulles pour échantillonner aléatoirement l’information visuelle à chaque essai (1000). Une régression a permis de déterminer quelle information était échantillonnée de façon systématique lors des essais où le participant a répondu correctement. Cette opération résulte en une image de classification pour chaque participant, montrant l’information visuelle utilisée. Enfin, grâce à une régression de deuxième ordre, nous avons pu déterminer quelles sont les régions du visage dont l’utilisation permet de prédire les habiletés dans quatre tâches différentes. Les résultats montrent que 59% de la variation dans les habiletés peut être expliquée grâce à l’utilisation de certaines régions du visage. Plus spécifiquement, plus les participants font usage systématiquement de la région de l’œil gauche du point de vue de l'observateur, plus ils sont habiles. / Abilities for face recognition largely vary among neurotypical individuals. The source of these variations remains largely unknown. Because use of visual information affects performance for a task, the main objective of the project was to better understand the way in which visual information is used affects abilities for face recognition. To this end, we have used the Bubbles method to evaluate use of information in neurotypical participants from the complete spectrum of abilities for face recognition, including extreme cases (developmental prosopagnosics and super-recognizers). Therefore, face recognition abilities were measured in 107 participants prior to evaluating the visual information they use. In 1000 trials where participants were asked to identify a celebrity’s face, visual information was spatially randomly sampled using the Bubbles method. A regression was then applied between the location of the sampled information and accuracy on each trial, determining which information was systematically sampled when participants correctly identified faces. A second-order regression was then used, which determined the utilization of which regions of the face predicts ability scores, measured in four different tests. Results show that 59% of variations in abilities can be explained by the use of visual information for face recognition. Specifically, the more systematically participants use the region of the left eye, the more accurate they tend to be.

La perception visuelle des visages est une fonction importante du cerveau humain, essentielle pour les interactions sociales. L’étude des bases neurales de la perception des visages a débuté il y a plusieurs décennies et les découvertes servent de modèle pour la compréhension de la perception visuelle en général. L’imagerie structurelle des patients présentant un déficit de reconnaissance des visages à la suite d’une lésion cérébrale a montré l’importance d’un vaste territoire au sein du cortex ventral occipito-temporal (VOTC), du lobe occipital jusqu’au lobe temporal antérieur (LTA), avec une prédominance droite. L’imagerie par résonnance magnétique fonctionnelle (IRMf) a montré l’existence de zones cérébrales circonscrites qui répondent plus fortement aux visages qu’aux autres objets visuels (organisation en « cluster ») principalement dans le VOTC postérieur. Cependant l’IRMf a été limitée dans sa capacité à retrouver de telles régions dans le lobe temporal antérieur à cause d’artefacts méthodologiques. Les études d’électro-encéphalographie intracrânienne (iEEG) réalisées chez les patients épileptiques sont une opportunité unique d’enregistrer l’activité neuronale directe avec un très haut rapport signal/bruit. Les études iEEG ont enregistré des réponses sélectives aux visages largement distribuées dans le VOTC, sans organisation en « clusters ». Malgré des années de recherches, plusieurs questions cruciales restent sans réponse : (1) quelle est l’organisation spatiale des régions sélectives aux visages (organisation distribuée vs. en « clusters ») ? ; (2) quelles sont les bases neurales de la perception des visages dans le LTA ? ; (3) quelles sont les régions critiques pour la perception des visages ? Afin de répondre à ces questions, nous avons utilisé les enregistrements et les stimulations électriques intracérébraux. Dans une 1ère étude (Jonas et al., sous presse), nous avons combiné les enregistrements iEEG avec la stimulation visuelle périodique rapide (FPVS). La méthode FPVS est basée sur le principe suivant : présenter des stimuli visuels à une fréquence fixe va générer une réponse EEG périodique à la même fréquence. Nous avons utilisé cette approche pour réaliser une cartographie complète des réponses sélectives aux visages dans le VOTC (28 participants). Nous leur avons montré des séquences d’images d’objets présentées à une fréquence fixe et rapide (6 Hz), avec un visage présenté tous les 5 objets (1,2 Hz). Les réponses sélectives aux visages ont été identifiées objectivement (à la fréquence de stimulation) et quantifiées dans tout le VOTC. Bien que ces réponses aient été enregistrées de manière largement distribuée, nous avons identifié plusieurs régions dans les lesquelles les réponses les plus fortes se regroupent spatialement (en « clusters »). De plus, nous avons enregistré la plus forte réponse dans le gyrus fusiforme droit. Enfin, nous avons enregistré des réponses sélectives aux visages dans 3 régions distinctes du LTA. Dans 3 autres études, nous rapportons de très rares cas de stimulations électriques de régions sélectives aux visages, testant leur rôle critique dans la perception des visages. Nous rapportons un cas de déficit transitoire de la perception des visages après stimulation du gyrus occipital inférieur droit, la région sélective au visage la plus postérieure (Jonas et al., 2012, 2014) et un cas similaire après stimulation du LTA (Jonas et al., 2015). Dans l’ensemble, ces études montrent que : (1) les régions impliquées dans la perception des visages sont largement distribuées le long du VOTC et certaines sont marquées par un regroupement spatial de leurs réponses les plus fortes ; (2) plusieurs régions distinctes sont sélectives aux visages dans le LTA; (3) des régions spécifiques dans le VOTC postérieur et le LTA sont critiques pour la perception des visages. Ces études montrent l’intérêt des enregistrements intracérébraux pour la compréhension des mécanismes de perception visuelle / Visual perception of faces is a primary function of the human brain, critical for social interactions. The neural basis of face perception in humans has been investigated extensively for decades as a primary research goal, whose findings may serve as a rich model for understanding perceptual recognition. Structural imaging of individuals with face recognition impairment following brain damage point to a large territory of the human ventral occipito-temporal cortex (VOTC), from the occipital lobe to the anterior temporal lobe (ATL), with a right hemispheric advantage. Functional magnetic resonance imaging studies (fMRI) have reported face-selective responses (larger responses to faces than other visual objects) in circumscribed regions (clustered organization) of the posterior VOTC. However, they failed to report genuine responses in the ATL because of methodological artefacts. Intracranial electroencephalographic (iEEG) recordings performed in epileptic patients offer a unique opportunity to measure direct local neural activity with a very high signal-to-noise ratio. In contrast to fMRI studies, iEEG studies recorded face-selective responses in widely distributed regions of the VOTC without any evidence of a clustered organization. Despite decades of research, several outstanding questions are still unanswered: (1) what is the spatial organization of brain regions supporting face perception (clustered vs. distributed)?; (2) what are the neural basis of face perception in the ATL?; (3) which are the critical regions for face perception? To address these gaps in knowledge, we used human iEEG recordings and electrical intracerebral stimulations. In a first study (Jonas et al., in press), we combined iEEG recordings with the Fast Periodic Visual Stimulation (FPVS), a powerful approach providing objective and high signal-to-noise brain responses. FPVS is based on the simple principle: presenting visual stimuli at a fixed rate generates a periodic EEG response at exactly the same frequency. We use this approach to report a comprehensive map of face-selective responses across the VOTC in a large group of participants (N=28). They were presented with natural images of objects at a rapid fixed rate (6 images per second: 6 Hz), with face stimuli interleaved as every 5th stimulus (i.e., 1.2 Hz). Face-selective responses were objectively (i.e., exactly at the face stimulation frequency) identified and quantified throughout the whole VOTC. Although face-selective responses were widely distributed, specific regions displayed a clustered spatial organization of their most face-selective responses. Among these regions, the right fusiform gyrus showed the largest face-selective response. In addition, we recorded face-selective responses in 3 distinct regions of the ATL. In 3 others studies, we reported very rare cases of intracerebral electrical stimulation of face-selective brain regions, testing the critical role of these regions in face perception. We reported a case of transient inability to recognize faces following the stimulation of the right inferior occipital gyrus, the most posterior face-selective region (Jonas et al., 2012, 2014) and one similar case following the stimulation of the right ATL (Jonas et al., 2015). Overall, these studies show that: (1) face-selective responses are widely distributed but some specific regions displayed a clustered spatial organization of their most face-selective responses; (2) several distinct regions are face-selective in the ATL; (3) specific brain regions in the posterior VOTC and in the ATL are critical for face perception. These finding also illustrate the diagnostic value of intracerebral electrophysiological recordings in understanding visual recognition processes