Face Representation Research Articles

Task-irrelevant inputs alter ensemble representations of faces within the spatial focus of attention.

Spatial attention enhances processing of information, but how does unattended and task-irrelevant information influence visual processing within the spatial focus of attention? We tested this by asking participants to extract the average emotional expression of a set of sequentially presented faces while simultaneously presenting task-irrelevant faces at a spatially unattended and task-irrelevant location. Across several experiments, we found that participants' reports of the emotional expression of faces at the attended location were biased toward the task-irrelevant faces. For example, when happier faces were presented at the unattended location, participants were biased to perceive the attended faces as happier. A control experiment in which participants were asked to also detect probes at cued and uncued locations showed that spatial attention was directed towards the cued location as instructed. Together, these results reveal that unattended and task-irrelevant inputs do not only affect the efficiency of target processing, for example by slowing responses or lowering accuracies, but that they can systematically bias ensemble representations within the spatial focus of attention. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Individual variation in the functional lateralization of human ventral temporal cortex: Local competition and long-range coupling.

The ventral temporal cortex (VTC) of the human cerebrum is critically engaged in computations related to high-level vision. One intriguing aspect of this region is its asymmetric organization and functional lateralization. Notably, in the VTC, neural responses to words are stronger in the left hemisphere, whereas neural responses to faces are stronger in the right hemisphere. Converging evidence has suggested that left-lateralized word responses emerge to couple efficiently with left-lateralized frontotemporal language regions, but evidence is more mixed regarding the sources of the right-lateralization for face perception. Here, we use individual differences as a tool to adjudicate between three theories of VTC organization arising from: 1) local competition between words and faces, 2) local competition between faces and other categories, 3) long-range coupling with VTC and frontotemporal areas subject to their own local competition. First, in an in-house functional MRI experiment, we demonstrated that individual differences in laterality are both substantial and reliable within a right-handed population of young adults. We found no (anti-)correlation in the laterality of word and face selectivity relative to object responses, and a positive correlation when using selectivity relative to a fixation baseline, challenging ideas of local competition between words and faces. We next examined broader local competition with faces using the large-scale Human Connectome Project (HCP) dataset. Face and tool laterality were significantly anti-correlated, while face and body laterality were positively correlated, consistent with the idea that generic local representational competition and cooperation may shape face lateralization. Last, we assessed the role of long-range coupling in the development of VTC laterality. Within our in-house experiment, substantial correlation was evident between VTC text laterality and several other nodes of a distributed text-processing circuit. In the HCP data, VTC face laterality was both negatively correlated with frontotemporal language laterality, and positively correlated with social perception laterality in the same areas, consistent with a long-range coupling effect between face and social processing representations, driven by local competition between language and social processing. We conclude that both local and long-range interactions shape the heterogeneous hemispheric specializations in high-level visual cortex.

Advanced facial recognition with LBP-URIGL hybrid descriptors

AbstractFacial recognition technology is transformative in security and human-machine interaction, reshaping societal interactions. Robust descriptors, essential for high precision in machine learning tasks like recognition and recall, are integral to this transformation. This paper presents a hybrid model enhancing local binary pattern descriptors for facial representation. By integrating rotation-invariant local binary pattern with uniform rotation-invariant grey-level co-occurrence, employing linear discriminant analysis for feature space optimization, and utilizing an artificial neural network for classification, the model achieves exceptional accuracy rates of 100% for Olivetti Research Laboratory, 99.98% for Maastricht University Computer Vision Test, and 99.17% for Extended Yale B, surpassing traditional methods significantly.

Faceify- Human Face Sketch to Real

The Human Face Sketch to Real Image Application using deep learning presents the development of an innovative application designed to convert human face sketches into realistic image using advanced deep learning techniques. The primary objective of this research is to bridge the gap between manual sketching and digital realism, offering a powerful tool for artists, law enforcement, and digital content creators. The proposed system leverages generative adversarial networks (GANs), particularly a variant known as the Sketch-to-Image GAN (SI-GAN), which is optimizes for translating sparse and abstract sketch lines into high-fidelity facial representations. The architecture consists of a generator network that refines sketches into realistic images, and a discriminator network that distinguishes between generated images and real photographs, continuously enhancing the model’s output. To ensure high accuracy and detail preservation, the model is trained on a diverse dataset comprising thousands of paired sketch and real face images. Advanced techniques such as attention mechanisms and multi-scale feature extraction are incorporated to maintain the integrity of facial features and textures. Additionally, a user friendly interface has been developed, allowing users to input sketches and receive high-quality realistic images in real-time. Evaluation metrics, including structural similarity index (SSIM) and Frechet Inception Distance (FID), Indicate that the proposed model outperforms existing state-of-the-art methods in both quality and realism. Keywords: Image Translation, Conditional Generative Adversarial Networks (CGANs), Deep Learning, Mobile Application Development, Backend Integration, Face Sketch, Photorealistic Image.

Crowding the face-space: The attractor field hypothesis and within-person variability

ABSTRACT Face space theory suggests that faces that are similar to others (i.e., typical faces) are represented in denser regions in face space than distinctive faces. Accordingly, typical face representations can be activated by the same input, leading to mistakenly identifying a person as someone else. A modification of this theory can also accommodate the opposite error in which two images of the same person are mistaken for different people, which results from intolerance to within-person variability. In two experiments, we tested two predictions of the modified theory: (1) greater tolerance of within-person variability should be observed for distinctive faces and (2) the same conditions that increase tolerance to within-person variability should facilitate differentiation of two similar-looking identities. The results support the first of these predictions, but not the second. The findings are interpreted in the context of attention to differences vs. commonalities when learning to distinguish faces from similar-looking others.

Fast updating of stimulus history reveals weak internal representations of faces in autism.

Atypical perception has been widely reported in autism spectrum disorders, and deficits in face recognition, specifically, are argued to be closely associated with social impairment experienced by these individuals. However, it is still debated (a) whether deficits are perceptually based, and (b)what the role is of experience-based refinements of perceptual face representations in autism. We investigated the effect of short- and long-term experienced stimulus history on face processing. Autistic and non-autistic individuals performed same-different judgments in a serial discrimination task where two consecutive faces were drawn from a distribution of morphed faces. Use of stimulus statistics was measured by testing the gravitation of face representations towards, the mean of a range of morphed faces around which they were sampled (regression-to-the-mean). The results show that unlike non-autistic individuals, representations of own- and other-race faces were equally biased by stimulus statistics in autistic individuals. Moreover, autistic individuals used the most recently exposed faces without forming a strong internal representation based on the overall experienced faces, indicating a weaker internal model of the "typical" averaged face. This accumulated history of faces may underlie typical face specialization, and thus may account for the reduced specialization for own-race faces shown in autism. The results shed light on the way autistic people process and recognize faces, and on the basic mechanisms underlying atypical face perception.

Face Recognition in the Modern Era: A Survey of Techniques, Challenges, and Innovations

This review paper talks about the development of face recognition, ranging from traditional methods to the latest methods of deep learning (Hasan et al., 2021; Sáez-Trigueros et al., 2018). The early methods were based on separate characteristics like SIFT and LBP (Balaban, 2015) that could not handle complex scenarios. The use of statistical subspace methods helped to improve face representation. Deep learning, however, has transformed the domain, where systems like DeepFace attain nearly human-like performance by taking advantage of vast and various datasets (Taigman et al., 2014). Nonetheless, the bias, fairness, and privacy issues that remain unsolved have led to the ongoing research to make the face recognition systems more robust and ethically acceptable. Keywords--- Face recognition; Illuminations; partial occlusion; pose invariance

View-symmetric representations of faces in human and artificial neural networks

View-symmetric representations of faces in human and artificial neural networks

Simultaneous activation of multiple face representations

Simultaneous activation of multiple face representations

Visualizing Face Representations after Adaptation

Visualizing Face Representations after Adaptation

Giant pyramidal neurons of the primary motor cortex express vasoactive intestinal polypeptide (VIP), a known marker of cortical interneurons

Vasoactive intestinal polypeptide (VIP) is known to be present in a subclass of cortical interneurons. Here, using three different antibodies, we demonstrate that VIP is also present in the giant layer 5 pyramidal (Betz) neurons which are characteristic of the limb and axial representations of the marmoset primary motor cortex (cytoarchitectural area 4ab). No VIP staining was observed in smaller layer 5 pyramidal cells present in the primary motor facial representation (cytoarchitectural area 4c), or in the premotor cortex (e.g. the caudal subdivision of the dorsal premotor cortex, A6DC), indicating the selective expression of VIP in Betz cells. VIP in Betz cells was colocalized with neuronal specific marker (NeuN) and a calcium-binding protein parvalbumin (PV). PV also intensely labelled axon terminals surrounding Betz cell somata. VIP-positive interneurons were more abundant in the superficial cortical layers and constituted about 5–7% of total cortical neurons, with the highest density observed in area 4c. Our results demonstrate the expression of VIP in the largest excitatory neurons of the primate cortex, which may offer new functional insights into the role of VIP in the brain, and provide opportunities for genetic manipulation of Betz cells.

Perceptual narrowing continues throughout childhood: Evidence from specialization of face processing

Face recognition shows a long trajectory of development and is known to be closely associated with the development of social skills. However, it is still debated whether this long trajectory is perceptually based and what the role is of experience-based refinements of face representations throughout development. We examined the effects of short and long-term experienced stimulus history on face processing, using regression biases of face representations towards the experienced mean. Children and adults performed same–different judgments in a serial discrimination task where two consecutive faces were drawn from a distribution of morphed faces. The results show that face recognition continues to improve after 9 years of age, with more pronounced improvements for own-race faces. This increased narrowing with age is also indicated by similar use of stimulus statistics for own-race and other-race faces in children, contrary to the different use of the overall stimulus history for these two face types in adults. Increased face proficiency in adulthood renders the perceptual system less tuned to other-race face statistics. Altogether, the results demonstrate associations between levels of specialization and the extent to which perceptual representations become narrowly tuned with age.

Rethinking Diaspora Subjects in the Narratives of North Korean Refugees-On the Representation of Faces and the Power of Resilience

Rethinking Diaspora Subjects in the Narratives of North Korean Refugees-On the Representation of Faces and the Power of Resilience

What is a compassionate face? Avoided negative affect explains differences between U.S. Americans and Chinese

ABSTRACT While a compassionate face for Germans mirrors others’ suffering, for U.S. Americans, a compassionate face is one that expresses a slight smile, partly because U.S. Americans want to avoid feeling negative (“avoided negative affect”; ANA) more than do Germans. The present work examines what people in a non-WEIRD (i.e. Chinese) cultural context think a compassionate face looks like. Additionally, it investigates whether an individually-measured cultural variable (i.e. ANA) can explain differences in conceptualisations of compassion between Chinese and U.S. Americans. Participants in China and the U.S. selected the face that most resembles a compassionate face in a reverse correlation task and completed a measure of ANA. As predicted, Chinese mental representations of a compassionate face included more sadness and less happiness compared to U.S. American mental representations of a compassionate face, and Chinese participants wanted to avoid feeling negative less than did U.S. Americans. Finally, ANA mediated the cultural differences in conceptualisations of compassion. We discuss how ANA and conceptualisations of compassion might be related to how people view the experience versus the expression of different emotions. This work has important implications for therapeutic settings and the meaning of compassion in an increasingly globalised and connected world.

Predictions enable top-down pattern separation in the macaque face-processing hierarchy

Distinguishing faces requires well distinguishable neural activity patterns. Contextual information may separate neural representations, leading to enhanced identity recognition. Here, we use functional magnetic resonance imaging to investigate how predictions derived from contextual information affect the separability of neural activity patterns in the macaque face-processing system, a 3-level processing hierarchy in ventral visual cortex. We find that in the presence of predictions, early stages of this hierarchy exhibit well separable and high-dimensional neural geometries resembling those at the top of the hierarchy. This is accompanied by a systematic shift of tuning properties from higher to lower areas, endowing lower areas with higher-order, invariant representations instead of their feedforward tuning properties. Thus, top-down signals dynamically transform neural representations of faces into separable and high-dimensional neural geometries. Our results provide evidence how predictive context transforms flexible representational spaces to optimally use the computational resources provided by cortical processing hierarchies for better and faster distinction of facial identities.

A Multidimensional Neural Representation of Face Impressions.

From a glimpse of a face, people form trait impressions that operate as facial stereotypes, which are largely inaccurate yet nevertheless drive social behavior. Behavioral studies have long pointed to dimensions of trustworthiness and dominance that are thought to underlie face impressions due to their evolutionarily adaptive nature. Using human neuroimaging (N = 26, 19 female, 7 male), we identify a two-dimensional representation of faces' inferred traits in the middle temporal gyrus (MTG), a region involved in domain-general conceptual processing including the activation of social concepts. The similarity of neural-response patterns for any given pair of faces in the bilateral MTG was predicted by their proximity in trustworthiness-dominance space, an effect that could not be explained by mere visual similarity. This MTG trait-space representation occurred automatically, was relatively invariant across participants, and did not depend on the explicit endorsement of face impressions (i.e., beliefs that face impressions are valid and accurate). In contrast, regions involved in high-level social reasoning (the bilateral temporoparietal junction and posterior superior temporal sulcus; TPJ-pSTS) and entity-specific social knowledge (the left anterior temporal lobe; ATL) also exhibited this trait-space representation but only among participants who explicitly endorsed forming these impressions. Together, the findings identify a two-dimensional neural representation of face impressions and suggest that multiple implicit and explicit mechanisms give rise to biases based on facial appearance. While the MTG implicitly represents a multidimensional trait space for faces, the TPJ-pSTS and ATL are involved in the explicit application of this trait space for social evaluation and behavior.

Stress Detection through Compound Facial Expressions Using Neural Networks

Human face is the most dynamic part of the body that conveys information about the instant emotions. Facial expression analysis starts from early 1900s where later on scientists identify the six basic facial expressions as Anger, Disgust, Fear, Happiness, Sadness, Surprise and Neutral with the pioneering studies of psychologists. In the last decades, the acceleration in artificial intelligence and computer vision research makes it possible to automatically detect facial expressions through images. Furthermore, micro expressions, muscle movements and compound facial expressions; that are the combinations of the basic expressions can be also analyzed with computer vision algorithms. The main motivation in automatic facial expression analysis is to support human-computer. Furthermore, facial expression analysis can be a driver for automatic emotion analysis. In this study, we propose a novel method to detect stress indicators on the frontal face images. The detection procedure is based on compound facial expression analysis. 49 couples of 6 basic facial expressions where one is dominating, and the other is the complementary expression are employed. iCV-MEFED facial expression dataset is used in the experiments where video and image samples are provided for every compound facial expression class. The training and testing of compound facial expressions are done using a deep neural network. The robust representations of faces are achieved using a fusion method that combines deep texture features and the action units on the face. Then, through the appropriate grouping of the compound expressions, the system can detect the signs of stress. The proposed approach obtains encouraging results, and it is open to further improvements.

Intergroup evaluative bias in facial representations of immigrants and citizens in the United States.

We used a reverse-correlation image-classification paradigm to visualize facial representations of immigrants and citizens in the United States. Visualizations of immigrants' faces were judged by independent raters as less trustworthy and less competent and were more likely to be categorized as a non-White race/ethnicity than were visualizations of citizens' faces. Additionally, image generators' personal characteristics (e.g., implicit and explicit evaluations of immigrants, nativity status) did not reliably track with independent judges' ratings of image generators' representations of immigrants. These findings suggest that anti-immigrant sentiment and racial/ethnic assumptions characterize facial representations of immigrants in the United States, even among people who harbor positivity toward immigrants.

The potential role of robust face representations learned within families when searching for one’s child in a crowd

Own child’s face is one of the most socially salient stimuli for parents, and a faster search for it than for other children’s faces may help provide warmer and more sensitive care. However, it has not been experimentally examined whether parents find their child’s face faster. In addition, although own child’s face is specially processed, the search time for own child’s face may be similar to that for other socially salient stimuli, such as own or spouse’s faces. This study tested these possibilities using a visual search paradigm. Participants (parents) searched for their child’s, own, spouse’s, other child’s, same-sex adult’s, or opposite-sex adult’s faces as search targets. Our findings indicate that both mothers and fathers identified their child’s face more quickly than other children’s faces. Similarly, parents found their own and spouse’s faces more quickly than other adults’ faces. Moreover, the search time for family members’ faces increased with the number of faces on the search display, suggesting an attentional serial search. These results suggest that robust face representations learned within families and close relationships can support reduced search times for family members’ faces.

Can names shape facial appearance?

Our given name is a social tag associated with us early in life. This study investigates the possibility of a self-fulfilling prophecy effect wherein individuals' facial appearance develops over time to resemble the social stereotypes associated with given names. Leveraging the face-name matching effect, which demonstrates an ability to match adults' names to their faces, we hypothesized that individuals would resemble their social stereotype (name) in adulthood but not in childhood. To test this hypothesis, children and adults were asked to match faces and names of children and adults. Results revealed that both adults and children correctly matched adult faces to their corresponding names, significantly above the chance level. However, when it came to children's faces and names, participants were unable to make accurate associations. Complementing our lab studies, we employed a machine-learning framework to process facial image data and found that facial representations of adults with the same name were more similar to each other than to those of adults with different names. This pattern of similarity was absent among the facial representations of children, thereby strengthening the case for the self-fulfilling prophecy hypothesis. Furthermore, the face-name matching effect was evident for adults but not for children's faces that were artificially aged to resemble adults, supporting the conjectured role of social development in this effect. Together, these findings suggest that even our facial appearance can be influenced by a social factor such as our name, confirming the potent impact of social expectations.