Identification of human faces

The human visual system appears to process the identity of faces separately from their emotional expression, whereas the human visual system does not appear to process emotional expression separately from identity. All current explanations of this visual processing asymmetry implicitly assume that it arises because of the organization of a specialized human face perception system. A second possibility is that this finding reflects general principles of perceptual processing. Studying animals that are unlikely to have evolved a specialized face perception system may shed fresh light on this issue. We report two experiments that investigated the interaction of identity and emotional expression in pigeons' perception of human faces. Experiment 1 found that pigeons perceive the similarity among faces sharing identity and emotion, and that these two dimensions are integral according to a spatial model of generalization. Experiment 2 found that pigeons' discrimination of emotion was reliably affected by irrelevant variations in identity, whereas pigeons' discrimination of identity was not reliably affected by irrelevant variations in emotion. Thus, the asymmetry previously reported in human studies was reproduced in our pigeon study. These results challenge the view that a specialized human face perception system must underlie this effect.

Face perception is probably the most developed visual perceptual skill in humans, most likely as a result of its unique evolutionary and social significance. Much recent research has converged to identify a host of relevant psychological mechanisms that support face recognition. In parallel, there has been substantial progress in uncovering the neural mechanisms that mediate rapid and accurate face perception, with specific emphasis on a broadly distributed neural circuit, comprised of multiple nodes whose joint activity supports face perception. This article focuses specifically on the neural underpinnings of face recognition, and reviews recent structural and functional imaging studies that elucidate the neural basis of this ability. In addition, the article covers some of the recent investigations that characterize the emergence of the neural basis of face recognition over the course of development, and explores the relationship between these changes and increasing behavioural competence. This paper also describes studies that characterize the nature of the breakdown of face recognition in individuals who are impaired in face recognition, either as a result of brain damage acquired at some point or as a result of the failure to master face recognition over the course of development. Finally, information regarding similarities between the neural circuits for face perception in humans and in nonhuman primates is briefly covered, as is the contribution of subcortical regions to face perception. WIREs Cogn Sci 2016, 7:247-263. doi: 10.1002/wcs.1388 For further resources related to this article, please visit the WIREs website.

Dogs have a rich social relationship with humans. One fundamental aspect of it is how dogs pay close attention to human faces in order to guide their behavior, for example, by recognizing their owner and his/her emotional state using visual cues. It is well known that humans have specific brain regions for the processing of other human faces, yet it is unclear how dogs’ brains process human faces. For this reason, our study focuses on describing the brain correlates of perception of human faces in dogs using functional magnetic resonance imaging (fMRI). We trained seven domestic dogs to remain awake, still and unrestrained inside an MRI scanner. We used a visual stimulation paradigm with block design to compare activity elicited by human faces against everyday objects. Brain activity related to the perception of faces changed significantly in several brain regions, but mainly in the bilateral temporal cortex. The opposite contrast (i.e., everyday objects against human faces) showed no significant brain activity change. The temporal cortex is part of the ventral visual pathway, and our results are consistent with reports in other species like primates and sheep, that suggest a high degree of evolutionary conservation of this pathway for face processing. This study introduces the temporal cortex as candidate to process human faces, a pillar of social cognition in dogs.

Humans and Macaques Employ Similar Face-Processing Strategies

Human face recognition is still a challenging task for computer-based machines mainly due to the similarity of human faces and unpredictable variations such as illumination, pose, expression, occlusion etc. A lot of algorithms have been developed to deal with these challenging issues in face recognition. One of the difficult problems is the geometric variation of face images. In some face recognition applications, only face images with uncontrolled format are available. Face images obtained from face detection stage may be directly used. Therefore, alignment or normalization is required to achieve better recognition performance. Eye detection technique is normally used to normalize face images. Invariant representations are used to improve the generalization of facial features. Feature-based and modelbased automatic face alignment can also be used to localize human faces more precisely. Illumination variation is another tough problem because human face is a 3D model and practical lighting conditions are complicated and unpredictable. Illumination normalization based on preprocessing, illumination invariant feature extraction and face modelling have been employed to reduce illumination variations or render faces under varying lighting conditions. The geometric on-the-plane rotation of a face image can be normalized by aforementioned methods. However, a face image with rotation in depth is not easily adjusted to a certain view. It is difficult to recognize faces in all poses with only training face images in limited views. One solution is to use 2D or 3D generative models to generate novel views of the face. Another method is based on the view-subspace learning. For facial expression variations,

In this study we assessed whether there is a single face space common to both human and cartoon faces by testing whether adaptation to cartoon faces can affect perception of human faces. Participants were shown Japanese animation cartoon videos containing faces with abnormally large eyes. The use of animated videos eliminated the possibility of position-dependent retinotopic adaptation (because the faces appear at many different locations) and more closely simulated naturalistic exposure. Adaptation to cartoon faces with large eyes significantly shifted preferences for human faces toward larger eyes, consistent with a common, non-retinotopic representation for both cartoon and human faces. This supports the possibility that there are representations that are specific to faces yet common to all kinds of faces.

The ability to recognize faces accurately and rapidly is an evolutionarily adaptive process. Most studies examining the neural correlates of face perception in adult humans have focused on a distributed cortical network of face-selective regions. There is, however, robust evidence from phylogenetic and ontogenetic studies that implicates subcortical structures, and recently, some investigations in adult humans indicate subcortical correlates of face perception as well. The questions addressed here are whether low-level subcortical mechanisms for face perception (in the absence of changes in expression) are conserved in human adults, and if so, what is the nature of these subcortical representations. In a series of four experiments, we presented pairs of images to the same or different eyes. Participants' performance demonstrated that subcortical mechanisms, indexed by monocular portions of the visual system, play a functional role in face perception. These mechanisms are sensitive to face-like configurations and afford a coarse representation of a face, comprised of primarily low spatial frequency information, which suffices for matching faces but not for more complex aspects of face perception such as sex differentiation. Importantly, these subcortical mechanisms are not implicated in the perception of other visual stimuli, such as cars or letter strings. These findings suggest a conservation of phylogenetically and ontogenetically lower-order systems in adult human face perception. The involvement of subcortical structures in face recognition provokes a reconsideration of current theories of face perception, which are reliant on cortical level processing, inasmuch as it bolsters the cross-species continuity of the biological system for face recognition.

Face perception in humans is governed more by right-hemispheric than left-hemispheric neural correlate. Some but not all neurophysiological studies depict a right-side dominance for face responsive neurons in the brains of macaques. Hence, it is an open question whether and to what extent a right-hemisphere preference of processing faces exists across primate brains. We investigated chimpanzees discriminating chimeric faces of chimpanzees and humans, i.e., the combination of either left or right sides of a face vertically flipped and merged into a whole face. We found an effect of choosing the left-chimeric face more often than the right-chimeric face as being the one of the two that is closer to the original face, reflecting an advantage for the right side of the brain to process faces, as reported in humans. Moreover, we found a modulation by age of the participants, suggesting that the exposure history with a particular category shapes the right-hemispheric neural correlate to a configural/holistic processing strategy. In other words, the findings in chimpanzee participants parallel those in human participants and are suggestive for similar neural machineries in the occipital-temporal cortices in both species.

Human face detection plays an important role in applications such as video surveillance, human computer interface, face recognition and face image database management. In this paper, we propose a novel facial feature detection algorithm for various face image types, conditions, invariant rotation, and any appearances. There are three main steps. First, Radon transform is used for face angle detection on rotated image. Subsequently, the feature regions are detected using Neural Visual Model (NVM). Finally, using image dilation and Radon transform, the facial features are extracted from the detected regions. Input parameters are obtained from the face characteristics and the positions of facial features not including any intensity informations. Our algorithm is successfully tested with various types of faces which are color images, gray images, binary images, wearing the sunglasses, wearing the scarf, lighting effect, low-quality images, color and sketch images from animated cartoon, rotated face images, and rendered face images.

Face Pareidolia in the Rhesus Monkey

The equivalence of two-dimensional PCA to line-based PCA

To mitigate privacy concerns, scrambling can be used to conceal face regions present in surveillance video content. Given that lightweight scrambling tools may not protect chroma information in order to limit bit rate overhead in heterogeneous usage environments, this paper investigates how the presence of non-scrambled chroma information in face regions influences the effectiveness of automatic and human face recognition (FR). To that end, we apply three automatic FR techniques to face images that have been privacy-protected by means of a layered scrambling technique developed for Motion JPEG XR, testing the effectiveness of automatic FR and layered scrambling using various experimental conditions. In addition, we investigate whether agreement exists between the judgments of 32 human observers and the output of automatic FR. Our experimental results demonstrate that human observers are not able to successfully recognize face images when simultaneously visualizing scrambled luma and non-scrambled chroma information. However, when an adversary has access to the coded bit stream structure, the presence of non-scrambled chroma information may significantly contribute to privacy leakage. By additionally applying layered scrambling to chroma information, our experimental results show that the amount of privacy leakage can be substantially decreased at the cost of an increase in bit rate overhead, and with the increase in bit rate overhead dependent on the number of scalability layers used.

Human face detection plays an important role in applications such as video surveillance, human computer interface, face recognition, and face image database management. We propose a face detection algorithm for color images in the presence of varying lighting compensation technique and we segment the skin color of the face image with the improved skin color model which is in the space of YCrCb, and then find the human face in a small area using the template matching method. The application on the skin color information is add to the face detection algorithm, the purpose of which is to pre-judge whether the image exist a face, thus we can exclude non-skin regions to reduce the space to search and improve the efficiency of the face detection algorithm. This paper proposed various adaptive templates to overcome the shortcomings of poor adaptability, the scale of the template can be seized to adjust the size of the area, so the adaptability of template can be increased.

<b>Problem statement:</b> The study presented in this study to combined face and ear algorithms as an application of human identification. Biometric system to the detection and identification of human faces and ears developed a multimodal biometric system using eigenfaces and eigenears. <b>Approach:</b> The proposed system used the extracted face and ear images to develop the respective feature spaces via the PCA algorithm called eigenfaces and eigenears, respectively. The proposed system showed promising results than individual face or ear biometrics investigated in the experiments. <b>Results:</b> The final achieve was then used to affirm the person as genuine or an impostor. System was tested on several databases and gave an overall accuracy of 92.24% with FAR of 10% and FRR of 6.1%. <b>Conclusion:</b> The results display if we combined face and ear is a good technique because it offered a high accuracy and security.

How well can human faces be identified by humans and by computers, using subjectively judged descriptions like long ears, wide-set eyes, etc.? Three classes of experiments are reported: 1) Gathering, analysis, and assessment of face-feature data for 255 faces. 2) Computer identification-studies. 3) Human identification-studies. A set of 22 features was evolved from an initially larger set to provide relevant, distinctive, relatively independent measures which can be judged reliably. Computer studies and a mathematical model established limits of performance of a person attempting to isolate a face from a population using feature descriptions. The model predicts that under certain conditions approximately 6 of an individual's features are required to isolate him from a population of 255. Human experiments under similar conditions showed unique identification occurred with an average of about 7 features. The model predicts that for a population of 4×106, only 14 feature-descriptions are required. These studies form a foundation for continuing research on real-time man-machine interaction for computer classification and identification of multidimensional vectors specified by noisy components.