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The idea that most of us are good at recognizing faces permeates everyday thinking and is widely used in the research literature. However, it is a correct characterization only of familiar-face recognition. In contrast, the perception and recognition of unfamiliar faces can be surprisingly error-prone, and this has important consequences in many real-life settings. We emphasize the variability in views of faces encountered in everyday life and point out how neglect of this important property has generated some misleading conclusions. Many approaches have treated image variability as unwanted noise, whereas we show how studies that use and explore the implications of image variability can drive substantial theoretical advances.

Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Using crowd-sourcing methods, we fused the judgments of multiple forensic facial examiners by averaging their rating-based identity judgments. Accuracy was substantially better for fused judgments than for individuals working alone. Fusion also served to stabilize performance, boosting the scores of lower-performing individuals and decreasing variability. Single forensic facial examiners fused with the best algorithm were more accurate than the combination of two examiners. Therefore, collaboration among humans and between humans and machines offers tangible benefits to face identification accuracy in important applications. These results offer an evidence-based roadmap for achieving the most accurate face identification possible.

Infants have asymmetrical exposure to different types of faces (e.g., more human than nonhuman, more female than male, and more own-race than other-race). What are the developmental consequences of such experiential asymmetry? Here, we review recent advances in research on the development of cross-race face processing. The evidence suggests that greater exposure to own-than other-race faces in infancy leads to developmentally early differences in visual preferences for, recognition of, formation of categories for, and scanning of own-and other-race faces. Further, such perceptual differences in infancy may be associated with the emergence of implicit racial bias, consistent with a perceptual-social linkage hypothesis. Current and future work derived from this hypothesis may lay an important empirical foundation for the development of intervention programs to combat the early occurrence of implicit racial bias.

Face pareidolia occurs when random or ambiguous inanimate objects are perceived as faces. While real faces automatically receive prioritized attention compared with nonface objects, it is unclear whether pareidolic faces similarly receive special attention. We hypothesized that, given the evolutionary importance of broadly detecting animacy, pareidolic faces may have enough faceness to activate a broad face template, triggering prioritized attention. To test this hypothesis, and to explore where along the faceness continuum pareidolic faces fall, we conducted a series of dot-probe experiments in which we paired pareidolic faces with other images directly competing for attention: objects, animal faces, and human faces. We found that pareidolic faces elicited more prioritized attention than objects, a process that was disrupted by inversion, suggesting this prioritized attention was unlikely to be driven by low-level features. However, unexpectedly, pareidolic faces received more privileged attention compared with animal faces and showed similar prioritized attention to human faces. This attentional efficiency may be due to pareidolic faces being perceived as not only face-like, but also as human-like, and having larger facial features—eyes and mouths—compared with real faces. Together, our findings suggest that pareidolic faces appear automatically attentionally privileged, similar to human faces. Our findings are consistent with the proposal of a highly sensitive broad face detection system that is activated by pareidolic faces, triggering false alarms (i.e., illusory faces), which, evolutionarily, are less detrimental relative to missing potentially relevant signals (e.g., conspecific or heterospecific threats). In sum, pareidolic faces appear “special” in attracting attention.

All cases presenting to the author have undergone surgical treatment and patients with middle ear disease and treated surgically within 2 months of presentation all showed some recovery in facial nerve function.

3D face recognition has become a trending research direction in both industry and academia. It inherits advantages from traditional 2D face recognition, such as the natural recognition process and a wide range of applications. Moreover, 3D face recognition systems could accurately recognize human faces even under dim lights and with variant facial positions and expressions, in such conditions 2D face recognition systems would have immense difficulty to operate. This paper summarizes the history and the most recent progresses in 3D face recognition research domain. The frontier research results are introduced in three categories: pose-invariant recognition, expression-invariant recognition, and occlusion-invariant recognition. To promote future research, this paper collects information about publicly available 3D face databases. This paper also lists important open problems.

Face detection is a priority of both the human and primate visual system. However, occasionally we misperceive faces in inanimate objects –– \"face pareidolia\". A key feature of these 'false positives' is that face perception occurs in the absence of visual features typical of real faces. Human faces are known to be located faster than objects in visual search. Here we used a visual search paradigm to test whether illusory faces share this advantage. Search times were faster for illusory faces than for matched objects amongst both matched (Experiment 1 ) and diverse (Experiment 2 ) distractors, however search times for real human faces were faster and more efficient than objects with or without an illusory face. Importantly, this result indicates that illusory faces are processed quickly enough by the human brain to confer a visual search advantage, suggesting the engagement of a broadly-tuned mechanism that facilitates rapid face detection in cluttered environments.

Face-processing deficits, while not required for the diagnosis of autism spectrum disorder (ASD), have been associated with impaired social skills—a core feature of ASD; however, the strength and prevalence of this relationship remains unclear. Across 445 participants from the NIMH Data Archive, we examined the relationship between Benton Face Recognition Test (BFRT) performance and Autism Diagnostic Observation Schedule-Social Affect (ADOS-SA) scores. Lower BFRT scores (worse face-processing performance) were associated with higher ADOS-SA scores (higher ASD severity)—a relationship that held after controlling for other factors associated with face processing, i.e., age, sex, and IQ. These findings underscore the utility of face discrimination, not just recognition of facial emotion, as a key covariate for the severity of symptoms that characterize ASD.

Masked face recognition (MFR) has emerged as a critical domain in biometric identification, especially with the global COVID-19 pandemic, which introduced widespread face masks. This survey paper presents a comprehensive analysis of the challenges and advancements in recognizing and detecting individuals with masked faces, which has seen innovative shifts due to the necessity of adapting to new societal norms. Advanced through deep learning techniques, MFR, along with face mask recognition (FMR) and face unmasking (FU), represents significant areas of focus. These methods address unique challenges posed by obscured facial features, from fully to partially covered faces. Our comprehensive review explores the various deep learning-based methodologies developed for MFR, FMR, and FU, highlighting their distinctive challenges and the solutions proposed to overcome them. Additionally, we explore benchmark datasets and evaluation metrics specifically tailored for assessing performance in MFR research. The survey also discusses the substantial obstacles still facing researchers in this field and proposes future directions for the ongoing development of more robust and effective masked face recognition systems. This paper serves as an invaluable resource for researchers and practitioners, offering insights into the evolving landscape of face recognition technologies in the face of global health crises and beyond.

Human vision is attuned to the subtle differences between individual faces. Yet we lack a quantitative way of predicting how similar two face images look and whether they appear to show the same person. Principal component–based three-dimensional (3D) morphable models are widely used to generate stimuli in face perception research. These models capture the distribution of real human faces in terms of dimensions of physical shape and texture. How well does a “face space” based on these dimensions capture the similarity relationships humans perceive among faces? To answer this, we designed a behavioral task to collect dissimilarity and same/different identity judgments for 232 pairs of realistic faces. Stimuli sampled geometric relationships in a face space derived from principal components of 3D shape and texture (Basel face model [BFM]). We then compared a wide range of models in their ability to predict the data, including the BFM from which faces were generated, an active appearance model derived from face photographs, and image-computable models of visual perception. Euclidean distance in the BFM explained both dissimilarity and identity judgments surprisingly well. In a comparison against 16 diverse models, BFM distance was competitive with representational distances in state-of-the-art deep neural networks (DNNs), including novel DNNs trained on BFM synthetic identities or BFM latents. Models capturing the distribution of face shape and texture across individuals are not only useful tools for stimulus generation. They also capture important information about how faces are perceived, suggesting that human face representations are tuned to the statistical distribution of faces.