What are superior face identity recognizers (SFIR) made of
TL;DR: In this paper, Meike Ramon proposes a stringent operational definition to identify people who excel at face identity recognition, i.e., super face identity recognizers (SFIR), based on difficulties at defining cases of prosopagnosia and prosopdysgnosia.
About: This article is published in Neuropsychologia.The article was published on 2021-07-30. It has received None citations till now.
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TL;DR: The observations clearly indicate that impairment in facial recognition, as assessed by the procedures utilized in the study, is rather closely associated with disease of the right hemisphere.
491 citations
TL;DR: It is found that optimal recognition performance is achieved with two fixations; performance does not improve with additional fixations, and the fixations made during face learning differ in location from thosemade during face recognition and are also more variable in duration; this suggests that different strategies are used for face learning and face recognition.
Abstract: It is well known that there exist preferred landing positions for eye fixations in visual word recognition. However, the existence of preferred landing positions in face recognition is less well established. It is also unknown how many fixations are required to recognize a face. To investigate these questions, we recorded eye movements during face recognition. During an otherwise standard face-recognition task, subjects were allowed a variable number of fixations before the stimulus was masked. We found that optimal recognition performance is achieved with two fixations; performance does not improve with additional fixations. The distribution of the first fixation is just to the left of the center of the nose, and that of the second fixation is around the center of the nose. Thus, these appear to be the preferred landing positions for face recognition. Furthermore, the fixations made during face learning differ in location from those made during face recognition and are also more variable in duration; thi...
355 citations
TL;DR: It is demonstrated that people vary in systematic ways, and that this variability is idiosyncratic-the dimensions of variability in one face do not generalize well to another, and this framework provides an explanation for various effects in face recognition.
159 citations
TL;DR: The history of face recognition technology, the current state-of-the-art methodologies, and future directions are presented, specifically on the most recent databases, 2D and 3D face recognition methods.
Abstract: Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera–subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.
155 citations
TL;DR: It is demonstrated that faces evolved to signal individual identity under negative frequency-dependent selection, and that social evolution has shaped patterns of human phenotypic and genetic diversity as well.
Abstract: Facial recognition plays a key role in human interactions, and there has been great interest in understanding the evolution of human abilities for individual recognition and tracking social relationships. Individual recognition requires sufficient cognitive abilities and phenotypic diversity within a population for discrimination to be possible. Despite the importance of facial recognition in humans, the evolution of facial identity has received little attention. Here we demonstrate that faces evolved to signal individual identity under negative frequency-dependent selection. Faces show elevated phenotypic variation and lower between-trait correlations compared with other traits. Regions surrounding face-associated single nucleotide polymorphisms show elevated diversity consistent with frequency-dependent selection. Genetic variation maintained by identity signalling tends to be shared across populations and, for some loci, predates the origin of Homo sapiens. Studies of human social evolution tend to emphasize cognitive adaptations, but we show that social evolution has shaped patterns of human phenotypic and genetic diversity as well.
109 citations