Processing Of Facial Information Research Articles

Neuroimaging studies of resting-state functional magnetic resonance imaging in eating disorders

Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), binge-eating disorder (BED), and pica, are psychobehavioral conditions characterized by abnormal eating behaviors and an excessive preoccupation with weight and body shape. This review examines changes in brain regions and functional connectivity in ED patients over the past decade (2013–2023) using resting-state functional magnetic resonance imaging (rs-fMRI). Key findings highlight alterations in brain networks such as the default mode network (DMN), central executive network (CEN), and emotion regulation network (ERN). In individuals with AN, there is reduced functional connectivity in areas associated with facial information processing and social cognition, alongside increased connectivity in regions linked to sensory stimulation, aesthetic judgment, and social anxiety. Conversely, BED patients show diminished connectivity in the dorsal anterior cingulate cortex within the salience network and increased connectivity in the posterior cingulate cortex and medial prefrontal cortex within the DMN. These findings suggest that rs-fMRI could serve as a valuable biomarker for assessing brain function and predicting treatment outcomes in EDs, paving the way for personalized therapeutic strategies.

Disrupted properties of functional brain networks in major depressive disorder during emotional face recognition: an EEG study via graph theory analysis.

Previous neuroimaging studies have revealed abnormal brain networks in patients with major depressive disorder (MDD) in emotional processing. While any cognitive task consists of a series of stages, little is yet known about the topology of functional brain networks in MDD for these stages during emotional face recognition. To address this problem, electroencephalography (EEG)-based functional brain networks of MDD patients at different stages of facial information processing were investigated in this study. First, EEG signals were collected from 16 patients with MDD and 18 age-, gender-, and education-matched normal subjects when performing an emotional face recognition task. Second, the global field power (GFP) method was employed to divide group-averaged event-related potentials into different stages. Third, using the phase transfer entropy (PTE) approach, the brain networks of MDD patients and normal individuals were constructed for each stage in negative and positive face processing, respectively. Finally, we compared the topological properties of brain networks of each stage between the two groups using graph theory approaches. The results showed that the analyzed three stages of emotional face processing corresponded to specific neurophysiological phases, namely, visual perception, face recognition, and emotional decision-making. It was also demonstrated that depressed patients showed abnormally decreased characteristic path length at the visual perception stage of negative face recognition and normalized characteristic path length in the stage of emotional decision-making during positive face processing compared to healthy subjects. Furthermore, while both the MDD and normal groups' brain networks were found to exhibit small-world network characteristics, the brain network of patients with depression tended to be randomized. Moreover, for patients with MDD, the centro-parietal region may lose its status as a hub in the process of facial expression identification. Together, our findings suggested that altered emotional function in MDD patients might be associated with disruptions in the topological organization of functional brain networks during emotional face recognition, which further deepened our understanding of the emotion processing dysfunction underlying MDD.

Evaluation of AI tools for healthcare networks at the cloud-edge interaction to diagnose autism in educational environments

Physical, social, and routine environments can be challenging for learners with autism spectrum disorder (ASD). ASD is a developmental disorder caused by neurological problems. In schools and educational environments, this disorder may not only hinder a child’s learning, but also lead to more crises and mental convulsions. In order to teach students with ASD, it is essential to understand the impact of their learning environment on their interaction and behavior. Different methods have been used to diagnose ASD in the past, each with their own strengths and weaknesses. Research into ASD diagnostics has largely focused on machine learning algorithms and strategies rather than diagnostic methods. This article discusses many diagnostic techniques used in the ASD literature, such as neuroimaging, speech recordings, facial features, and EEG signals. This has led us to conclude that in schools and educational settings, autism can be diagnosed cheaply, quickly, and accurately through face analysis. To facilitate and speed up the processing of facial information among children in educational settings, we applied the AlexNet architecture designed for edge computing. A fast method for detecting autism spectrum disorders from the face can be applied to educational settings using this structure. While we have investigated a variety of methods, the face can provide us with appropriate information about the disorder. In addition, it can produce more interpretive features. In order to help students in schools who are suffering from this disease, key factors must be considered: potential clinical and therapeutic situations, efficiency, predictability, privacy protection, accuracy, cost-effectiveness, and lack of methodological intervention. The diseases are troublesome, so they should be identified and treated.

When less is not more: the effect of transparent masks on facial attractiveness judgment

During the COVID-19 pandemic, face masks have been widely used in daily life. Previous studies have suggested that faces wearing typical masks that occlude the lower half of the face are perceived as more attractive than face without masks. However, relatively little work has been done on how transparent masks that reveal the lower half of the face affect the judgment of facial attractiveness. To investigate the effect of transparent masks on the perceived attractiveness, in the current study, we asked participants to rate the attractiveness of faces without masks and with a typical opaque mask and a transparent mask. The results showed that faces wearing opaque masks were evaluated as more attractive than those wearing transparent masks or no masks. The benefit of opaque masks was more pronounced in faces that were initially evaluated as unattractive. Interestingly, wearing transparent masks decreased the perceived attractiveness of faces but only for the faces initially rated as attractive, possibly because of the visual distortion of the lower half of the face by transparent masks. In summary, we found that opaque and transparent masks have different effects on perceived attractiveness, depending on the attractiveness of faces. Given benefits of transparent masks in socio-emotional and cognitive processing, it would be important to further understand the effect of transparent masks on face information processing.

Analysis of attentional biases in anxiety using 24 facial priming sequences.

The processing of emotional facial expressions helps people to adjust to the physical and social environment. Furthermore, mental disorders such as anxiety have been linked to attentional biases in the processing of this type of information. Nevertheless, there are still contradictory results that might be due to the methodology used and to individual differences in the manifestation of anxiety. Our research goal was to use 24 facial priming sequences to analyse attentional biases in the detection of facial expressions of fear, considering the levels and the ways in which individuals express anxiety. With higher levels of cognitive anxiety and general trait anxiety, those sequences that began in the upper half (vs. lower half) elicited a speedier response in the detection of fear. The results are discussed within the context of other techniques and disorders that prompt a deficit in the processing of facial information.

Effects of spatially filtered fearful faces and awareness on amygdala activity in adults with autism spectrum disorder: A magnetoencephalography study

BackgroundThe amygdala is pivotal in emotional face processing. Spatial frequencies (SFs) of visual images are divided and processed via two visual pathways: low spatial frequency (LSF) information is conveyed by the magnocellular pathway, while the parvocellular pathway carries high spatial frequency information. We hypothesized that altered amygdala activity might underlie atypical social communication caused by changes in both conscious and non-conscious emotional face processing in the brain in individuals with autism spectrum disorder (ASD). MethodEighteen adults with ASD and 18 typically developing (TD) peers participated in this study. Spatially filtered fearful- and neutral-expression faces and object stimuli were presented under supraliminal or subliminal conditions, and neuromagnetic responses in the amygdala were measured using 306-channel whole-head magnetoencephalography. ResultsThe latency of the evoked responses at approximately 200 ms to unfiltered neutral face stimuli and object stimuli in the ASD group was shorter than that in the TD group in the unaware condition. Regarding emotional face processing, the evoked responses in the ASD group were larger than those in the TD group under the aware condition. The later positive shift during 200–500 ms (ARV) was larger than that in the TD group, regardless of awareness. Moreover, ARV to HSF face stimuli was larger than that to the other spatial filtered face stimuli in the aware condition. ConclusionRegardless of awareness, ARV might reflect atypical face information processing in the ASD brain.

Assessing the coupling between local neural activity and global connectivity fluctuations: Application to human intracranial electroencephalography during a cognitive task.

Cognitive-relevant information is processed by different brain areas that cooperate to eventually produce a response. The relationship between local activity and global brain states during such processes, however, remains for the most part unexplored. To address this question, we designed a simple face-recognition task performed in patients with drug-resistant epilepsy and monitored with intracranial electroencephalography (EEG). Based on our observations, we developed a novel analytical framework (named "local-global" framework) to statistically correlate the brain activity in every recorded gray-matter region with the widespread connectivity fluctuations as proxy to identify concurrent local activations and global brain phenomena that may plausibly reflect a common functional network during cognition. The application of the local-global framework to the data from three subjects showed that similar connectivity fluctuations found across patients were mainly coupled to the local activity of brain areas involved in face information processing. In particular, our findings provide preliminary evidence that the reported global measures might be a novel signature of functional brain activity reorganization when a stimulus is processed in a task context regardless of the specific recorded areas.

EEG decoding of multidimensional information from emotional faces

Humans can detect and recognize faces quickly, but there has been little research on the temporal dynamics of the different dimensional face information that is extracted. The present study aimed to investigate the time course of neural responses to the representation of different dimensional face information, such as age, gender, emotion, and identity. We used support vector machine decoding to obtain representational dissimilarity matrices of event-related potential responses to different faces for each subject over time. In addition, we performed representational similarity analysis with the model representational dissimilarity matrices that contained different dimensional face information. Three significant findings were observed. First, the extraction process of facial emotion occurred before that of facial identity and lasted for a long time, which was specific to the right frontal region. Second, arousal was preferentially extracted before valence during the processing of facial emotional information. Third, different dimensional face information exhibited representational stability during different periods. In conclusion, these findings reveal the precise temporal dynamics of multidimensional information processing in faces and provide powerful support for computational models on emotional face perception.

Far from the eyes, far from the heart: COVID-19 confinement dampened sensitivity to painful facial features.

In the last 2 years, governments of many countries imposed heavy social restrictions to contain the spread of the COVID-19 virus, with consequent increase of bad mood, distress, or depression for the people involved. Few studies investigated the impact of these restrictive measures on individual social proficiency, and specifically the processing of emotional facial information, leading to mixed results. The present research aimed at investigating systematically whether, and to which extent, social isolation influences the processing of facial expressions. To this end, we manipulated the social exclusion experimentally through the well-known Cyberball game (within-subject factor), and we exploited the occurrence of the lockdown for the Swiss COVID-19 first wave by recruiting participants before and after being restricted at home (grouping factor). We then tested whether either form of social segregation influenced the processing of pain, disgust, or neutral expressions, across multiple tasks probing access to different components of affective facial responses (state-specific, shared across states). We found that the lockdown (but not game-induced exclusion) affected negatively the processing of pain-specific information, without influencing other components of the affective facial response related to disgust or broad unpleasantness. In addition, participants recruited after the confinement reported lower scores in empathy questionnaires. These results suggest that social isolation affected negatively individual sensitivity to other people's affect and, with specific reference to the processing of facial expressions, the processing of pain-diagnostic information.

Early Influence of Emotional Scenes on the Encoding of Fearful Expressions With Different Intensities: An Event-Related Potential Study.

Contextual affective information influences the processing of facial expressions at the relatively early stages of face processing, but the effect of the context on the processing of facial expressions with varying intensities remains unclear. In this study, we investigated the influence of emotional scenes (fearful, happy, and neutral) on the processing of fear expressions at different levels of intensity (high, medium, and low) during the early stages of facial recognition using event-related potential (ERP) technology. EEG data were collected while participants performed a fearful facial expression recognition task. The results showed that (1) the recognition of high-intensity fear expression was higher than that of medium- and low-intensity fear expressions. Facial expression recognition was the highest when faces appeared in fearful scenes. (2) Emotional scenes modulated the amplitudes of N170 for fear expressions with different intensities. Specifically, the N170 amplitude, induced by high-intensity fear expressions, was significantly higher than that induced by low-intensity fear expressions when faces appeared in both neutral and fearful scenes. No significant differences were found between the N170 amplitudes induced by high-, medium-, and low-intensity fear expressions when faces appeared in happy scenes. These results suggest that individuals may tend to allocate their attention resources to the processing of face information when the valence between emotional context and expression conflicts i.e., when the conflict is absent (fear scene and fearful faces) or is low (neutral scene and fearful faces).

Early visual exposure primes future cross-modal specialization of the fusiform face area in tactile face processing in the blind

The fusiform face area (FFA) is a core cortical region for face information processing. Evidence suggests that its sensitivity to faces is largely innate and tuned by visual experience. However, how experience in different time windows shape the plasticity of the FFA remains unclear. In this study, we investigated the role of visual experience at different time points of an individual's early development in the cross-modal face specialization of the FFA. Participants (n = 74) were classified into five groups: congenital blind, early blind, late blind, low vision, and sighted control. Functional magnetic resonance imaging data were acquired when the participants haptically processed carved faces and other objects. Our results showed a robust and highly consistent face-selective activation in the FFA region in the early blind participants, invariant to size and level of abstraction of the face stimuli. The cross-modal face activation in the FFA was much less consistent in other groups. These results suggest that early visual experience primes cross-modal specialization of the FFA, and even after the absence of visual experience for more than 14 years in early blind participants, their FFA can engage in cross-modal processing of face information.

Converting consumer-generated content into an innovation resource: A user ideas processing framework in online user innovation communities

• Propose a User Ideas Processing Framework (UIPF) to help enterprises efficiently process user ideas in OUICs. • Propose a new IDEA vectorization method and use it to represent text for clustering user ideas. • Use User Idea Cluster Algorithm (UICA) and theme analysis to discover idea content among massive user-generated content. • Use the UIPF to identify innovative ideas about the issues that are heatedly discussed by consumers but ignored by companies. An Online User Innovation Community (OUIC) is a space for consumers to share product usage experiences and put forward product improvement suggestions. However, as an increasing number of consumers post content in OUICs, companies face information processing challenges. Based on Organizational Information Processing Theory (OIPT), this study proposes a User Ideas Processing Framework (UIPF) to help enterprises efficiently process user ideas in OUICs and then applies it to a sample of 5,889 ideas from the Salesforce Idea Exchange. The case study results show that a UIPF can solve the information overload problem. Specifically, in Part 1 of the UIPF, we propose a new IDEA vectorization method and use it to cluster user ideas. Then, theme analysis is conducted on clusters to summarize the idea content in OUICs. This step gives us an overview of the information in OUICs. Compared with the standardized methods, our IDEA vectorization method can obtain better clustering results. Then, Part 2 of the UIPF builds a logistic regression model to identify innovative ideas from clusters. Compared with the famous “3C” method, the innovative ideas selected by the UIPF are more suitable for consumer requirements. In conclusion, the UIPF can help enterprises process information efficiently in OUICs.

Spontaneous Visual Preference for Face-Like Stimuli Is Impaired in Newly-Hatched Domestic Chicks Exposed to Valproic Acid During Embryogenesis.

Faces convey a great amount of socially relevant information related to emotional and mental states, identity and intention. Processing of face information is a key mechanism for social and cognitive development, such that newborn babies are already tuned to recognize and orient to faces and simple schematic face-like patterns since the first hours of life. Similar to neonates, also non-human primates and domestic chicks have been shown to express orienting responses to faces and schematic face-like patterns. More importantly, existing studies have hypothesized that early disturbances of these mechanisms represent one of the earliest biomarker of social deficits in autism spectrum disorders (ASD). We used VPA exposure to induce neurodevelopmental changes associated with ASD in domestic chicks and tested whether VPA could impact the expression of the animals’ approach responses to schematic face-like stimuli. We found that VPA impairs the chicks’ preference responses to these social stimuli. Based on the results shown here and on previous studies, we propose the domestic chick as animal model to investigate the biological mechanisms underlying face processing deficits in ASD.

Multiple feature fusion‐based video face tracking for IoT big data

With the advancement of Internet of Things (IoT) and artificial intelligence technologies, and the need for rapid application growth in fields, such as security entrance control and financial business trade, facial information processing has become an important means for achieving identity authentication and information security. However, in the process of acquiring facial feature information, face information is easily affected by factors, such as object occlusion, lighting changes, and similar backgrounds. In this paper, we propose a multifeature fusion algorithm based on integral histograms and a real-time update tracking particle filtering (PF) module. First, edge features and colour features are extracted, weighting methods are used to weight the colour histogram and edge features to describe facial features, and fusion of colour features and edge features is made adaptive by using fusion coefficients to improve face tracking reliability. Then, the integral histogram is integrated into the PF algorithm to simplify the calculation steps of complex particles and improve operational efficiency. Finally, the tracking window size is adjusted in real-time according to the change in the average distance from the particle centre to the edge of the current model and the initial model to reduce the drift problem and achieve stable tracking with significant changes in the target dimension. The results show that the algorithm improves video tracking accuracy, simplifies particle operation complexity, improves the speed, and has good anti-interference ability and robustness compared with extracting a single feature.

Do facially disfiguring features influence attention and perception of faces? Evidence from an antisaccade task.

Facial disfigurements can influence how observers attend to and interact with the person, leading to disease-avoidance behaviour and emotions (disgust, threat, fear for contagion). However, it is unclear whether this behaviour is reflected in the effect of the facial stigma on attention and perceptual encoding of facial information. We addressed this question by measuring, in a mixed antisaccade task, observers’ speed and accuracy of orienting of visual attention towards or away from peripherally presented upright and inverted unfamiliar faces that had either a realistic looking disease-signalling feature (a skin discolouration), a non-disease-signalling control feature, or no added feature. The presence of a disfiguring or control feature did not influence the orienting of attention (in terms of saccadic latency) towards upright faces, suggesting that avoidance responses towards facial stigma do not occur during covert attention. However, disfiguring and control features significantly reduced the effect of face inversion on saccadic latency, thus suggesting an impact on the holistic processing of facial information. The implications of these findings for the encoding and appraisal of facial disfigurements are discussed.

A Study on Intelligent Obscured Face Image Completion System Based on GAN Network

Image recognition and processing is an important part of modern information technology. However, in real life, the face will inevitably be blocked by hats, masks, or face photos are damaged, scratches, and images are added mosaic and other reasons, resulting in face image masking, increasing face information recognition and processing difficulties. Image completion is a kind of technology to restore the missing information of obscured image. By supplementing the information of missing area, it can reduce the difficulty of image recognition and simplify the processing of face information.So it has important research significance for the restoration of shaded image. The traditional image completion technology produces the face of poor natural degree. At present, most of the popular completion methods combine depth learning, and the repair methods based on generating anti-network GAN are the representatives of them.But the stability of the network is poor, so it is difficult to train pictures with large sheltered area or poor image quality.Some methods are not effective in the completion of arbitrary shaded areas. Therefore, this study focuses on the improvement of the stability of the generated adversarial network and the maintenance of the global and local semantic consistency between the completion image and the real image. The main work includes: (1) Sets a special network structure of global discriminator and local discriminator. When the input is arbitrarily shaded, the completion image can maintain the consistency of global and local semantics. (2) The least square loss function is used to improve the stability of the GAN network. So it performs great when the input image has large area masking or low visualization degree. (3) ADAM algorithm is used to accelerate the training of neural network. (4) Based on the completion model, a human-computer interaction software is designed and developed, which includes data set selection, image selection, image completion, result evaluation and so on. The system is applied to the removal of facial structures such as wrinkles and spots. Compared with some existing software, the system can effectively remove the selected facial structures without image distortion. Based on the design of special network structure, the system improves the supervision ability of global and local completion, the stability of GAN network and enhances its training ability in large area masking. The system is intelligent and has strong completion ability for arbitrary masking and large area masking. The research results of this paper effectively restore the information loss caused by image masking and prepare favorable conditions for subsequent image recognition and processing.

Holistic face processing is influenced by non-conscious visual information.

Holistic face processing has been widely implicated in conscious face perception. Yet, little is known about whether holistic face processing occurs when faces are processed unconsciously. The present study used the composite face task and continuous flash suppression (CFS) to inspect whether the processing of target facial information (the top half of a face) is influenced by irrelevant information (the bottom half) that is presented unconsciously. Results of multiple experiments showed that the composite effect was observed in both monocular and CFS conditions, providing the first evidence that the processing of top facial halves is influenced by the aligned bottom halves no matter whether they are presented consciously or unconsciously. However, much of the composite effect for faces without masking was disrupted when bottom facial parts were rendered with CFS. These results suggest that holistic face processing can occur unconsciously, but also highlight the significance of holistic processing of consciously presented faces.

Case of Asperger's Syndrome and Lesion of the Right Amygdala: Deficits in Implicit and Explicit Fearful Face Recognition.

Introduction: Studies of brain-damaged patients revealed that amygdala lesions cause deficits in the processing and recognition of emotional faces. Patients with autism spectrum disorders (ASD) have similar deficits also related to dysfunctions of the limbic system including the amygdala.Methods: We investigated a male patient who had been diagnosed with Asperger's syndrome. He also presented with a lesion of the right mesial temporal cortex, including the amygdala. We used functional magnetic resonance imaging (fMRI) to investigate neuronal processing during a passive viewing task of implicit and explicit emotional faces. Clinical assessment included a facial emotion recognition task.Results: There was no amygdala activation on both sides during the presentation of masked emotional faces compared to the no-face control condition. Presentation of unmasked happy and angry faces activated the left amygdala compared to the no-face control condition. There was no amygdala activation in response to unmasked fearful faces on both sides. In the facial emotion recognition task, the patient biased positive and neutral expressions as negative.Conclusions: This case report describes a male patient with right amygdala damage and an ASD. He displayed a non-response of the amygdala to fearful faces and tended to misinterpret fearful expressions. Moreover, a non-reactivity of both amygdalae to emotional facial expressions at an implicit processing level was revealed. It is discussed whether the deficient implicit processing of facial emotional information and abnormalities in fear processing could contribute and aggravate the patient's impairments in social behavior and interaction.

Facial Expression Recognition With Two-Branch Disentangled Generative Adversarial Network

Facial Expression Recognition (FER) is a challenging task in computer vision as features extracted from expressional images are usually entangled with other facial attributes, e.g., poses or appearance variations, which are adverse to FER. To achieve a better FER performance, we propose a model named Two-branch Disentangled Generative Adversarial Network (TDGAN) for discriminative expression representation learning. Different from previous methods, TDGAN learns to disentangle expressional information from other unrelated facial attributes. To this end, we build the framework with two independent branches, which are specific for facial and expressional information processing respectively. Correspondingly, two discriminators are introduced to conduct identity and expression classification. By adversarial learning, TDGAN is able to transfer an expression to a given face. It simultaneously learns a discriminative representation that is disentangled from other facial attributes for each expression image, which is more effective for FER task. In addition, a self-supervised mechanism is proposed to improve representation learning, which enhances the power of disentangling. Quantitative and qualitative results in both in-the-lab and in-the-wild datasets demonstrate that TDGAN is competitive to the state-of-the-art methods.

Sexual dimorphism in hemispheric processing of faces in humans: A meta-analysis of 817 cases.

A well-established neuroimaging literature predicts a right-sided asymmetry in the activation of face-devoted areas such as the fusiform gyrus (FG) and its resulting M/N170 response during face processing. However, the face-related response sometimes appears to be bihemispheric. A few studies have argued that bilaterality depended on the sex composition of the sample. To shed light on this matter, two meta-analyses were conducted starting from a large initial database of 250 ERP (Event-related potentials)/MEG (Magnetoencephalography) peer-reviewed scientific articles. Paper coverage was from 1985 to 2020. Thirty-four articles met the inclusion criteria of a sufficiently large and balanced sample size with strictly right-handed and healthy participants aged 18–35 years and N170 measurements in response to neutral front view faces at left and right occipito/temporal sites. The data of 817 male (n = 414) and female (n = 403) healthy adults were subjected to repeated-measures analyses of variance. The results of statistical analyses from the data of 17 independent studies (from Asia, Europe and America) seem to robustly indicate the presence of a sex difference in the way the two cerebral hemispheres process facial information in humans, with a marked right-sided asymmetry of the bioelectrical activity in males and a bilateral or left-sided activity in females.