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The ability to accurately decode others' facial expressions is essential for successful social interaction. Previous theories suggest that aspects of parental emotionality-the frequency, persistence and intensity of parents' own emotions-can influence children's emotion perception. Through a combination of mechanisms, parental emotionality may shape how children's brains specialize to respond to emotional expressions, but empirical data are lacking. The present study provides a direct empirical test of the relation between the intensity, persistence and frequency of parents' own emotions and children's neural responses to perceiving emotional expressions. Event-related potentials (ERPs) were recorded as typically developing 3- to 5-year-old children (final Ns = 59 and 50) passively viewed faces expressing different emotional valences (happy, angry and fearful) at full and reduced intensity (100% intense expression and 40% intense expression). We examined relations between parental emotionality and children's mean amplitude ERP N170 and negative central responses. The findings demonstrate a clear relation between parental emotionality and children's neural responses (in the N170 mean amplitude and latency) to emotional expressions and suggest that parents may influence children's emotion-processing neural circuitry.

Faces are among the most important visual stimuli that humans perceive in everyday life. While extensive literature has examined emotional processing and social evaluations of faces, most studies have examined either topic using unimodal approaches. In this review, we promote the use of multimodal cognitive neuroscience approaches to study these processes, using two lines of research as examples: ambiguity in facial expressions of emotion and social trait judgment of faces. In the first set of studies, we identified an event-related potential that signals emotion ambiguity using electroencephalography and we found convergent neural responses to emotion ambiguity using functional neuroimaging and single-neuron recordings. In the second set of studies, we discuss how different neuroimaging and personality-dimensional approaches together provide new insights into social trait judgments of faces. In both sets of studies, we provide an in-depth comparison between neurotypicals and people with autism spectrum disorder. We offer a computational account for the behavioral and neural markers of the different facial processing between the two groups. Finally, we suggest new practices for studying the emotional processing and social evaluations of faces. All data discussed in the case studies of this review are publicly available.

Facial micro-expression (ME), consisting of uncontrollable muscle movements in faces, is an important clue for revealing real people's feelings. Due to the short duration and low intensity, the salient feature representation learning is the main challenge for robust facial ME recognition. To acquire the diverse and spatial relation representation, this paper proposes a simple and yet distinctive micro-expression recognition model based on multiscale convolutional fusion and multi-channel capsule network (MCFMCN). Firstly, the apex frame in a ME clip, located by computing the pixel difference between frames, is filtered by the optical flow transformation. Secondly, a multiscale fusion module is introduced to capture diverse ME related details. Then, to further explore the subtle spatial relations between parts in the ME faces, the multi-channel capsule network is designed to improve the feature representation performance of the traditional single channel capsule network. Finally, the entire ME recognition model is trained and verified on three benchmarks (CASMEII, SAMM, and SMIC) using the associated standard evaluation protocols: unweighted average recall rate (UAR) and unweighted F1 score (UF1). ME recognition experiments indicate that our method based on MCFMCN can improve the UAR (from 75.79% to 83.58%) and UF1(from79.37% to 87.06%) in comparison with the traditional capsule network. Extensive experimental results show the performance of proposed ME recognition is superior to that of works based on pervious single channel capsule network or other state-of-the-art CNN models, which validates the finding that combination of multi-scale analysis and multi-channel capsule network is feasible and effective to improve the ME recognition performance. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) is a developmental condition resulting from abnormalities in brain structure and function, which can manifest as communication and social interaction difficulties. Conventional methods for diagnosing ASD may not be effective in the early stages of the disorder. Hence, early diagnosis is crucial to improving the patient's overall health and well-being. One alternative and effective method for early autism diagnosis is facial expression recognition since autistic children typically exhibit distinct facial expressions that can aid in distinguishing them from other children. This paper provides a deep convolutional neural network (DCNN)-based real-time emotion recognition system for autistic kids. The proposed system is designed to identify six facial emotions, including surprise, delight, sadness, fear, joy, and natural, and to assist medical professionals and families in recognizing facial expressions in autistic children for early diagnosis and intervention. In this study, an attention-based YOLOv8 (AutYOLO-ATT) algorithm for facial expression recognition is proposed, which enhances the YOLOv8 model's performance by integrating an attention mechanism. The proposed method (AutYOLO-ATT) outperforms all other classifiers in all metrics, achieving a precision of 93.97%, recall of 97.5%, F1-score of 92.99%, and accuracy of 97.2%. These results highlight the potential of the proposed method for real-world applications, particularly in fields where high accuracy is essential. [ABSTRACT FROM AUTHOR]

Dynamic ensemble pruning (DEP) can select the best set of models for an unseen sample to improve a classification system's prediction accuracy and robustness. However, little work has been done to apply DEP to the facial expression recognition (FER) system and reduce DEP's computational burden. This paper proposes a semi-dynamic ensemble pruning algorithm (SDEP) to implement the FER task. First, a subspace-based classifier sequences selection method is proposed to break DEP's neighborhood construction limitation and avoid the exhaustive similarity calculation. Moreover, a pre-prediction mechanism is introduced to calculate the relationship between an unseen sample and data subspaces. Ultimately, the calculated relationship scores and their credibility will be used as the weights for fusion. SDEP reduces the DEP's computational burden while retaining the dynamic characteristics through changing the classifier selection into the subspace-based classifier sequences combination. SDEP demonstrates superior classification accuracy and efficiency to several state-of-the-art dynamic and static EP algorithms in benchmark FER datasets Fer2013, JAFFE, CK+ using a homogeneous ensemble of 799 CNN classifiers. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is an essential field for intelligent human-computer interaction, but the COVID-19 pandemic has made unimodal techniques less effective due to masks. Multimodal approaches that combine information from multiple modalities are more robust at recognizing emotions from facial expressions. The need to accurately recognize human emotions based on facial expressions is still significant. The study proposed a multimodal methodology based on deep learning for facial recognition under masks and vocal expressions. The proposed approach used two standard datasets, M-LFW-F and CREMA-D to capture facial and vocal emotional cues. The resulting dataset was used to train a multimodal neural network using fusion techniques that outperformed unimodal methods. The proposed approach achieved an accuracy of 79.05%, while the unimodal approach achieved 68.76%, demonstrating that the proposed approach outperforms unimodal techniques in facial expression recognition under masked conditions. This highlights the potential of multimodal techniques for improving FER in challenging scenarios. [ABSTRACT FROM AUTHOR]

Image captioning has emerged as a prominent approach for generating verbal descriptions of images that humans can read and understand. Numerous techniques and models in this domain have predominantly focused on analyzing the factual elements present within an image, employing convolutional neural networks (CNN) and long short-term memory (LSTM) networks to generate captions. However, an inherent limitation of these existing approaches is their failure to consider the emotional aspects exhibited by the main subject within an image, thereby potentially leading to inaccuracies in reflecting the conveyed emotional content. Acknowledging this limitation, this paper endeavors to construct an improved model dedicated to extracting human emotions from images and seamlessly embedding emotional attributes into the accompanying captions. In our research, we employ the widely accessible benchmarking image captioning dataset, Flickr8k. Our ultimate objective is to establish a more appropriate and impactful model for images containing human faces that provide more accurate and impacting captions. [ABSTRACT FROM AUTHOR]

Humans express emotions through various modalities such as facial expressions and natural language. However, the relationships between emotions expressed through different modalities and their correlations with neural activities remain uncertain. Here, we aimed to unveil some of these uncertainties by investigating the similarity of emotion representations across modalities and brain regions. First, we represented various emotion categories as multi-dimensional vectors derived from visual (face), linguistic, and visio-linguistic data, and used representational similarity analysis to compare these modalities. Second, we examined the linear transferability of emotion representation from other modalities to the visual modality. Third, we compared the representational structure derived in the first step with those from brain activities across 360 regions. Our findings revealed that emotion representations share commonalities across modalities with modality-type dependent variations, and they can be linearly mapped from other modalities to the visual modality. Additionally, emotion representations in uni-modalities showed relatively higher similarity with specific brain regions, while multi-modal emotion representation was most similar to representations across the entire brain region. These findings suggest that emotional experiences are represented differently across various brain regions with varying degrees of similarity to different modality types, and that they may be multi-modally conveyable in visual and linguistic domains. [ABSTRACT FROM AUTHOR]

Background: The use of self-report pain scales in persons with aphasia can be challenging due to communication and cognitive problems, while for assessing pain self-report pain is considered the gold standard (Harrison RA, Field TS. Post stroke pain: identification, assessment, and therapy. Cerebrovasc Dis. 2015;39(3–4):190–201.). An observational scale may be used as an alternative. This study examines the validity and reliability of the observational Pain Assessment in Impaired Cognition (PAIC15) scale in persons with aphasia. Methods: Persons with aphasia were observed during rest and transfer by two observers using the PAIC15. The PAIC15 comprises 15 items covering the three domains of facial expressions, body movements, and vocalizations. When able, the participant completed four self-report pain scales after each observation. The observations were repeated within one week. For criterion validity, correlations between the PAIC15 and self-report pain scales were calculated and for construct validity, three hypotheses were tested. Reliability was determined by assessing internal consistency, and intra- and interobserver agreement. Results: PAIC15 observations were obtained for 71 persons (mean age 75.5 years) with aphasia. Fair positive correlations (rest: 0.35–0.50; transfer: 0.38–0.43) were reported between PAIC15 and almost all self-report pain scales. Results show that significantly more pain was observed in persons with aphasia during transfer than during rest. No differences were found for observed pain between persons with aphasia who use pain medication and those without, or persons who have joint diseases compared to those without. Results showed acceptable internal consistency. Intra- and interobserver agreement was high for most PAIC15 items, particularly for the domains body movements and vocalizations during rest and transfer. Conclusions: Recognition of pain in persons aphasia using the PAIC15 showed mixed yet promising results. [ABSTRACT FROM AUTHOR]

Assessing pain in newborns in the NICU is crucial due to their frequent exposure to painful stimuli, yet it's challenging due to the subjective nature of current methods. This study aimed to evaluate the effectiveness of an AI system designed for automatic facial recognition by comparing its performance with the expert opinion of health care provider. This is a secondary analysis from an eye-tracking study, assessing neonatal pain evaluations by healthcare professionals. The performance of AI software, FaceReader 9, was compared to experts' evaluations using a visual-analog scale, focusing on identifying specific facial action units associated with different pain levels. The study found significant differences in AI-generated metrics—arousal and valence—across three stimulus types: non-noxious thermal, short-noxious, and prolonged-noxious, with p-values below 0.001. A strong correlation (r = 0.84, p ≤.001) was observed between AI metrics and expert ratings. Eleven facial action units were identified as relevant to describe neonatal pain. The findings highlight the AI system's potential in accurately detecting and analyzing newborn facial expressions in response to varying pain intensities, demonstrating a significant correlation with healthcare professionals' assessments. This suggests that AI technology could enhance objective pain assessment in neonates. [ABSTRACT FROM AUTHOR]

Background: Individuals on the autism spectrum commonly have differences from non-autistic people in expressing their emotions using communicative behaviors, such as facial expressions. However, it is not yet clear if this reduced expressivity stems from reduced physiological reactivity in emotional contexts or if individuals react internally, but do not show these reactions externally to others. We hypothesized that autism is characterized by a discordance between in-the-moment internal psychophysiological arousal and external communicative expressions of emotion. Methods: Forty-one children on the autism spectrum and 39 non-autistic, typically developing (TD) children of two age groups (2–4 and 8–12 years) participated in a low-level stress task whilst wearing a wireless electrocardiogram. Children's negative emotional expressions (facial, vocal, bodily) were coded following standardized protocols. Alexithymia traits were assessed using the Children's Alexithymia Measure with school-aged children only. Data analyses involved ANOVAs, correlations, and sensitivity analyses. Results: There were no group differences in physiological arousal (heart rate) or in communicative expressions of stress to the stress task. For TD preschoolers, physiological arousal during the stress task was associated with vocal expressions and for TD school-aged children, they were associated with facial and bodily expressions. By contrast, for children on the autism spectrum, physiological arousal during the stress tasks was not associated with communicative expressions across age groups. Conclusions: Our findings suggest that children on the autism spectrum might experience emotional disconcordance, in that their physiological arousal does not align with their communicative expressions. Therefore, the internally experienced stress of children on the autism spectrum may be inadvertently missed by teachers and caregivers and, consequently, learning opportunities for teaching emotional communication and regulation may be also missed. Our results support the use of wearable biosensors to facilitate such interventions in children on the autism spectrum. [ABSTRACT FROM AUTHOR]

Simulation theories predict that the observation of other's expressions modulates neural activity in the same centres controlling their production. This hypothesis has been developed by two models, postulating that the visual input is directly projected either to the motor system for action recognition (motor resonance) or to emotional/interoceptive regions for emotional contagion and social synchronization (emotional resonance). Here we investigated the role of frontal/insular regions in the processing of observed emotional expressions by combining intracranial recording, electrical stimulation and effective connectivity. First, we intracranially recorded from prefrontal, premotor or anterior insular regions of 44 patients during the passive observation of emotional expressions, finding widespread modulations in prefrontal/insular regions (anterior cingulate cortex, anterior insula, orbitofrontal cortex and inferior frontal gyrus) and motor territories (Rolandic operculum and inferior frontal junction). Subsequently, we electrically stimulated the activated sites, finding that (i) in the anterior cingulate cortex and anterior insula, the stimulation elicited emotional/interoceptive responses, as predicted by the 'emotional resonance model'; (ii) in the Rolandic operculum it evoked face/mouth sensorimotor responses, in line with the 'motor resonance' model; and (iii) all other regions were unresponsive or revealed functions unrelated to the processing of facial expressions. Finally, we traced the effective connectivity to sketch a network-level description of these regions, finding that the anterior cingulate cortex and the anterior insula are reciprocally interconnected while the Rolandic operculum is part of the parieto-frontal circuits and poorly connected with the former. These results support the hypothesis that the pathways hypothesized by the 'emotional resonance' and the 'motor resonance' models work in parallel, differing in terms of spatio-temporal fingerprints, reactivity to electrical stimulation and connectivity patterns. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) in the wild is one of the most challenging visual tasks owing to various uncontrolled factors such as occlusion, pose, and subtle variation in real scenes. These factors can directly affect the robust performance of current networks, especially as most single-feature learning space methods lack the extraction of potential discriminative features and fail to provide a deeper understanding of expressions. To address the above issues, we propose a novel hybrid attention-aware learning network (HALNet), which comprises a feature compactness network (FCN), a hybrid attention enhancement network (HAEN), and a joint loss optimization strategy. First, FCN performs basic expression feature extraction and optimizes intra- and inter-class distributions simultaneously. Afterward, HAEN constructs a multi-level feature enhancement space by fusing hybrid attention based on CNN and transformer in parallel to effectively improve the profound understanding of expressions. Finally, the expression classification is performed by supervised optimization with joint loss. Extensive experiments are assessed on some of the widest employed wild expression datasets, and results indicate our method is superior to several present state-of-the-art methods, obtaining accuracies of 90.29%, 90.04%, and 61.75% on RAF-DB, FERPlus, and AffectNet, respectively. The cross-dataset and occlusion and pose variation datasets assessment further substantiate our approach's sound generalization and robustness. [ABSTRACT FROM AUTHOR]

The ability of a person to communicate with other people and engage with the outside environment makes it crucial to a person's existence. It can be challenging for all the people who have hearing loss to communicate without the aid of lip reading, facial expression, or signals. Also, without instruction, it is challenging for them to comprehend lip movements, which makes it challenging for them to hear spoken words. The visual speech recognition devices for lip reading were developed to solve this issue and help the deaf understand speech. Recently, Visual Voice Recognition research has focused a lot on deep learning advancements. Here, we suggested the LSTM and CNN 3D hybrid model for MIRACL VC1 Dataset visual speech recognition. When a person's lip features are extracted for pattern detection, the text without audio is converted in this process. A model used to automatically recognize speech is called visual speech recognition. The proposed work stands apart from the previous work by incorporation the hybrid model, the combination of the models would yield a better and efficient accuracy rate comparatively and also the focal novelty of the proposed work was that the concatenation of the extracted features to provide an input to the system was a challenging one so has to manage the weights in the entire process of extraction for a huge number of dataset. It was suggested that two separate models, CNN 3D and LSTM, be combined to create hybrid model in mandate to progress the overall accuracy in this case. In comparison with the individual CNN 3D model, whose testing accuracy is 79%, and the LSTM model, whose accuracy was 85% when we employed the English Dataset, the hybrid model's training, test, and validation accuracy were all 98, 85, and 86%, respectively. [ABSTRACT FROM AUTHOR]

Deep learning techniques have attained substantial progress in various face‐related tasks, such as face recognition, face inpainting, and facial expression recognition. To prevent infection or the spread of the virus, wearing of masks in public places has been mandated following the COVID‐19 epidemic, which has led to face occlusion and posed significant challenges for face recognition systems. Most prominent masked face recognition solutions rely on mask segmentation tasks. Therefore, segmentation can be used to mitigate the negative impacts of wearing a mask and improve recognition accuracy. Mask region segmentation suffers from two main problems: there is no standard type of masks that people wear, they come in different colours and designs, and there is no publicly available masked face dataset with appropriate ground truth for the mask region. In order to address these issues, we propose an encoder–decoder framework that utilizes a boundary‐aware attention network combined with a new hybrid loss to provide a map, patch, and pixel‐level supervision. We also introduce a dataset called MFSD, with 11,601 images and 12,758 masked faces for masked face segmentation. Furthermore, we compare the performance of different cutting‐edge deep learning semantic segmentation models on the presented dataset. Experimental results on the MSFD dataset reveal that the suggested approach outperforms state‐of‐the‐art, algorithms with 97.623% accuracy, 93.814% IoU, and 96.817% F1‐score rate. Our dataset of masked faces with mask region labels and source code will be available online. [ABSTRACT FROM AUTHOR]

Bilateral transcutaneous auricular vagus nerve stimulation (taVNS) - a non-invasive neuromodulation technique – has been investigated as a safe and feasible technique to treat many neuropsychiatric conditions. such as epilepsy, depression, anxiety, and chronic pain. Our aim is to investigate the effect of taVNS on neurophysiological processes during emotional and Go/No-Go tasks, and changes in frontal alpha asymmetry. We performed a randomized, double-blind, sham-controlled trial with 44 healthy individuals who were allocated into two groups (the active taVNS group and the sham taVNS group). Subjects received one session of taVNS (active or sham) for 60 min. QEEG was recorded before and after the interventions, and the subjects were assessed while exposed to emotional conditions with sad and happy facial expressions, followed by a Go/No-Go trial. The results demonstrated a significant increase in N2 amplitude in the No-Go condition for the active taVNS post-intervention compared to the sham taVNS after adjusting by handedness, mood, and fatigue levels (p = 0.046), significantly reduced ERD during sad conditions after treatment (p = 0.037), and increased frontal alpha asymmetry towards the right frontal hemisphere during the emotional task condition (p = 0.046). Finally, we observed an interesting neural signature in this study that suggests a bottom-up modulation from brainstem/subcortical to cortical areas as characterized by improved lateralization of alpha oscillations towards the frontal right hemisphere, and changes in ERP during emotional and Go/No-Go tasks that suggests a better subcortical response to the tasks. Such bottom-up effects may mediate some of the clinical effects of taVNS. [ABSTRACT FROM AUTHOR]

Guided by theories of communicative disenfranchisement (TCD) and memorable messages (ToMM), this study analyzes 60 open-ended survey responses detailing experiences of grief following the onset of invisible, physical illness (e.g., chronic overlapping pain conditions, autoimmune rheumatic diseases). Employing reflexive thematic analysis, we identify (1) illness-related losses that are disenfranchised by discourses surrounding grief and (2) opportunities to enfranchise these losses via memorable messages. Situated at two points in time, before and after the onset of illness, we identify disenfranchised losses as what once was (e.g., physical [in]dependence, relational connection, images of self, and trust in institutions) and what will never be (e.g., family planning and career dreams). Next, we identify opportunities to disrupt this disenfranchisement through memorable messages of presence (e.g., nonverbal acknowledgment and verbal expressions of belief) and finding the fit (e.g., physical restructuring and community connection). In doing so, we extend theoretical understanding of both memorable messages, in the form that they can take (of experience) and as points of (dis)enfranchisement, and communicative (dis)enfranchisement. This study contributes to a growing body of health communication research on disenfranchising experiences in chronic illness by highlighting a resulting experience of illness not often talked about – illness-related loss and its grief process. Theoretical and practical implications, as well as limitations and future directions, are discussed. [ABSTRACT FROM AUTHOR]

Paranoid ideation is a transdiagnostic construct that is associated with social impairment and often occurs in psychotic spectrum disorders. Little research has examined how paranoid ideation is related to social behaviors that underlie social impairment and may ultimately lead to social rejection. It is important to consider that negative symptoms and sleep problems also contribute to social impairment. No research has assessed the unique and combined influence of paranoid ideation, negative symptoms, and sleep problems on social impairment. Therefore, the current study examined how paranoid ideation, negative symptoms, and sleep problems contribute to poorer social skills and social rejection in a transdiagnostic sample of persons with psychosis and community members (N = 112). Assessments included diagnostic and symptom interviews, questionnaires, behavioral ratings of social skill and facial displays of affect, and naive observer reactions utilizing thin-slice methodology. Greater paranoid ideation, negative symptoms, and sleep problems were each related to poorer social skill and more negative reactions from observers. When considered in path analyses, negative symptoms were associated with observer reports of less willingness to interact with participants through poorer social skill. These findings demonstrate the symptom correlates of social rejection and how interpersonal behavior may contribute to social exclusion. [ABSTRACT FROM AUTHOR]

Aim: To identify specific facial expressions associated with pain behaviors using the PainChek application in residents with dementia. Design: This is a secondary analysis from a study exploring the feasibility of PainChek to evaluate the effectiveness of a social robot (PARO) intervention on pain for residents with dementia from June to November 2021. Methods: Participants experienced PARO individually five days per week for 15 min (once or twice) per day for three consecutive weeks. The PainChek app assessed each resident's pain levels before and after each session. The association between nine facial expressions and the adjusted PainChek scores was analyzed using a linear mixed model. Results: A total of 1820 assessments were completed with 46 residents. Six facial expressions were significantly associated with a higher adjusted PainChek score. Horizontal mouth stretch showed the strongest association with the score, followed by brow lowering parting lips, wrinkling of the nose, raising of the upper lip and closing eyes. However, the presence of cheek raising, tightening of eyelids and pulling at the corner lip were not significantly associated with the score. Limitations of using the PainChek app were identified. Conclusion: Six specific facial expressions were associated with observational pain scores in residents with dementia. Results indicate that automated real‐time facial analysis is a promising approach to assessing pain in people with dementia. However, it requires further validation by human observers before it can be used for decision‐making in clinical practice. Impact: Pain is common in people with dementia, while assessing pain is challenging in this group. This study generated new evidence of facial expressions of pain in residents with dementia. Results will inform the development of valid artificial intelligence‐based algorithms that will support healthcare professionals in identifying pain in people with dementia in clinical situations. Reporting Method: The study adheres to the CONSORT reporting guidelines. Patient or Public Contribution: One resident with dementia and two family members of people with dementia were consulted and involved in the study design, where they provided advice on the protocol, information sheets and consent forms, and offered valuable insights to ensure research quality and relevance. Trial Registration: Australian and New Zealand Clinical Trials Registry number (ACTRN12621000837820). [ABSTRACT FROM AUTHOR]

The human brain is fundamentally a social brain, with its perceptual and cognitive systems evolved to support a diverse set of processes aimed at enabling our complex social function. And while in the last decades the field has advanced considerably in understanding the mechanisms by which the perceptual and cognitive faculties support human social behaviour, there remains a lack of knowledge about how social information is communicated between individuals. In my work, I have studied nonverbal visual social signalling via faces and eyes. In this article, I summarize the results from three lines of my research which show that (a) limiting face visibility decreases its social value, (b) eyes transmit key social messages, and (c) group size modulates social messages. Together, these data show that visual signals from faces and eyes convey complex social messages and represent an important vehicle for the communication of social information. [ABSTRACT FROM AUTHOR]

Wearing facial masks became a common practice worldwide during the COVID-19 pandemic. This study investigated (1) whether facial masks that cover adult faces affect 4- to 6-year-old children's recognition of emotions in those faces and (2) whether the duration of children's exposure to masks is associated with emotion recognition. We tested children from Switzerland (N = 38) and Brazil (N = 41). Brazil represented longer mask exposure due to a stricter mandate during COVID-19. Children had to choose a face displaying a specific emotion (happy, angry, or sad) when the face wore either no cover, a facial mask, or sunglasses. The longer hours of mask exposure were associated with better emotion recognition. Controlling for the hours of exposure, children were less likely to recognise emotions in partially hideen faces. Moreover, Brazilian children were more accurate in recognising happy faces than Swiss children. Overall, facial masks may negatively impact children's emotion recognition. However, prolonged exposure appears to buffer the lack of facial cues from the nose and mouth. In conclusion, restricting facial cues due to masks may impair kindergarten children's emotion recognition in the short run. However, it may facilitate their broader reading of facial emotional cues in the long run. [ABSTRACT FROM AUTHOR]

Apart from verbal communication among humans, non-verbal interactions also play a significant role in conveying meaningful information. Non-verbal cues mainly comprise gestures, body postures, and facial expressions. Hand gestures constitute the preferred mechanism for non-verbal communication, and today, they also find utility in human–computer interaction (HCI), gaming, virtual reality, robotics, sign language, etc. While extensive research has been conducted on utilizing deep learning for hand gesture recognition, there has been a notable scarcity of efforts focused on leveraging the sparse characteristics of deeply acquired features to distinguish hand postures, even in the presence of challenges such as varying hand sizes, diverse spatial positions within images, and background clutter. We demonstrate the effect of data augmentation, transfer learning, and sparsity on the performance of the proposed algorithm using publicly available hand gesture datasets. We also provide a quantitative comparative analysis of the proposed approach with state-of-the-art algorithms for static hand gesture recognition. We illustrate a noteworthy finding wherein dictionary learning through LC-KSVD, when applied to fine-tuned features extracted from a deep architecture, outperforms the results achieved by state-of-the-art architectures in the context of hand gesture classification. We have realized substantial enhancements with our proposed methodology when compared to a baseline convolutional model. For instance, in the case of the EgoGesture dataset, we attained an accuracy of 94.9 % , as opposed to the baseline accuracy of 63.3 % , through the utilization of sparsity in deep features. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) plays a vital role for applications based on human–computer interaction. In the past few years, many deep learning models have been proposed for FER, but their performance is limited due to challenges such as variation in head pose, occlusion, illumination, etc. Moreover, existing models consider input image through holistic view without giving attention to features relevant to expressions. In this work, we propose a deep fusion framework for FER which employs self-attention mechanism to resolve this issue. Furthermore, to improve feature representation of the images, the proposed model transforms the input image to wavelet domain through discrete wavelet transform. The framework employs two parallel branches for shallow and deep features which are fused for better feature representation. The proposed model is evaluated on posed and in-the-wild datasets: CK+, JAFFE, MUG, YALE, RAF, and experimental results validate the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Objective: Anorexia nervosa (AN) has been associated with alterations in the processing of socio‐emotional information, including impairments in the recognition of emotions in other people's faces. However, adolescents with AN might not show the impairments found in adult patients. The present study investigated facial emotion recognition in adolescents with AN, aiming to replicate our previous results of superior emotion recognition abilities in adolescents with AN compared to adolescents without mental disorders. Method: Adolescent girls (12–18 years) with AN (n = 33) were compared to girls without mental disorders (n = 41). Participants completed one task requiring identification of emotions (happy, sad, afraid, angry, neutral) in faces and one control task. Results: As expected, adolescents with AN showed superior emotion recognition, with higher accuracy rates specifically for afraid faces. Conclusion: This is the first study replicating previous results on basic emotion recognition in adolescents with AN using (almost) the same methodology. The results suggest that, in contrast to adults, adolescents with AN do not show impairments in facial emotion recognition. The impairments may arise in the longer course of the illness, however, longitudinal studies are necessary to confirm this assumption. Highlights: Adolescent girls with anorexia nervosa (AN) did not show impairments in facial emotion recognition compared to adolescents girls with no mental disordersOn the contrary, adolescent with AN showed superior emotion recognition specifically for afraid faces (and a trend for sad faces)The results replicate those of a previous study and suggest that emotion recognition impairments may arise with prolonged illness duration [ABSTRACT FROM AUTHOR]

Objective: The study investigated interpersonal distance in patients with anorexia nervosa (AN), focussing on the role of other's facial expression and morphology, also assessing physiological and subjective responses. Method: Twenty‐nine patients with AN and 30 controls (CTL) were exposed to virtual characters either with an angry, neutral, or happy facial expression or with an overweight, normal‐weight, or underweight morphology presented either in the near or far space while we recorded electrodermal activity. Participants had to judge their preferred interpersonal distance with the characters and rated them in terms of valence and arousal. Results: Unlike CTL, patients with AN exhibited heightened electrodermal activity for morphological stimuli only, when presented in the near space. They also preferred larger and smaller interpersonal distances with overweight and underweight characters respectively, although rating both negatively. Finally, and similar to CTL, they preferred larger interpersonal distance with angry than neutral or happy characters. Discussion: Although patients with AN exhibited behavioural response to emotional stimuli similar to CTL, they lacked corresponding physiological response, indicating emotional blunting towards emotional social stimuli. Moreover, they showed distinct behavioural and physiological adjustments in response to body shape, confirming the specific emotional significance attached to body shape. Highlights: Patients with anorexia nervosa (AN) show a regulation of interpersonal distances to social emotional stimuli similar to healthy controls (CTL) but lack the corresponding physiological response.Patients with AN, not CTL, prefer shorter interpersonal distance with underweight social stimuli and larger interpersonal distance with overweight ones and show increased physiological response to both of them.Patients with AN show a bias towards morphological rather than emotional cues when processing information to optimise social interaction. [ABSTRACT FROM AUTHOR]

This study examined the effects of an intervention package using high-tech augmentative and alternative communication (AAC) with Naturalistic Instruction (NI), time delay, and prompting during social routines for preschoolers on the autism spectrum. The AAC was a communication application on a tablet programmed with color photo representations of participants and communication partners engaged in social routines. The primary dependent measure was a linguistic communication turn on the AAC device completing the instructor's cloze phrase related to the routine. A secondary dependent measure was prelinguistic communication –nonspecific vocalizations and smiling – within each turn of the routine. Results indicated that all communication types increased and maintained for all three participants with two participants meeting mastery criteria within the minimal number of sessions. These results are significant in providing additional evidence for the effectiveness of AAC intervention beyond teaching simple object requests and in teaching more socially meaningful communication to children on the autism spectrum. Additionally, this study supports the use of color photo representations to promote social interaction. Future research should continue to evaluate such interventions on a larger scale and include naturally occurring communication partners, such as siblings and peers. [ABSTRACT FROM AUTHOR]

DailyConnect is a visual-based mobile application that supports children with autism spectrum disorder (ASD) in recalling memories by reviewing photos through discrete trial training (DTT) to understand situation-based emotions. To assess DailyConnect and its adaptability to a child's characteristics and emotional situations, a pilot study was conducted that included 15 children with ASD and their parents and teachers. The DTT steps—memory recall and situation recognition, emotion recognition, emotion cues, facial expression recognition, and response behavior—were reliable in assessing the understanding of emotional situations when compared before and after the intervention in four categories of emotional situations, namely, happiness, sadness, anger, and fear. The results revealed that DailyConnect improves the understanding of situation-based emotions, particularly negative emotions (e.g., sadness: mean diff = −.687, sig. <.01, T = −3.866, d = 1.006; anger: mean diff = −.952, sig. <.01, T = −6.187, d =.705; fear: mean diff = −.961, sig. <.01, T = −5.522, d =.627); however, its effectiveness varied for children in different emotional situations. Furthermore, subjective feedback from participants and users (parents and teachers) provided insights into design considerations for similar mobile aids. [ABSTRACT FROM AUTHOR]

Although the development of prosocial behavior has been widely studied from the behavioral aspect, the neural mechanisms underlying prosocial behavior in the early stages of development remain unclear. Therefore, this study investigated the neural mechanisms underlying the emergence of prosocial behavior in 3-year-old children. Brain activity in the medial pFC and right TPJ (rTPJ) and facial expression activity, which are related to the ability to infer others' mental states (mentalizing), during the observation of prosocial and antisocial scenes were measured using functional near-infrared spectroscopy and electromyography, respectively. Subsequently, the children's helping and comforting behaviors toward an experimenter were assessed to examine prosocial behavioral tendencies. A correlation analysis revealed that the children who showed stronger activity levels in the rTPJ while observing prosocial scenes had more immediate helping behaviors toward others than those who did not show stronger response levels. Moreover, the amount of facial expression activity correlated with prosocial behavior, including both helping and comforting behaviors. These results suggest that the development of mentalizing ability and the social evaluation of others' actions, mediated by the rTPJ, contribute to the emergence of prosocial behavior. [ABSTRACT FROM AUTHOR]

The aim of the current study was to examine facial and body posture emotion recognition among deaf and hard-of-hearing (DHH) and hearing young adults. Participants were (N = 126) DHH (n = 48) and hearing (n = 78) college students who completed two emotion recognition tasks in which they were shown photographs of faces and body postures displaying different emotions of both high and low intensities and had to infer the emotion being displayed. Compared to hearing participants, DHH participants performed worse on the body postures emotion task for both high and low intensities. They also performed more poorly on the facial emotion task, but only for low-intensity emotional facial expressions. On both tasks, DHH participants whose primary mode of communication was Signed English performed significantly more poorly than those whose primary mode was American Sign Language (ASL) or spoken English. Moreover, DHH participants who communicated using ASL performed similarly to hearing participants. This suggests that difficulties in affect recognition among DHH individuals occur when processing both facial and body postures that are more subtle and reflective of real-life displays of emotion. Importantly, this also suggests that ASL as a primary form of communication in this population may serve as a protective factor against emotion recognition difficulties, which could, in part, be due to the complex nature of this language and its requirement to perceive meaning through facial and postural expressions with a wide visual lens. [ABSTRACT FROM AUTHOR]

Depictions of sadness are commonplace, and here we aimed to discover and catalogue the complex and nuanced ways that people interpret sad facial expressions. We used a rigorous qualitative methodology to build a thematic framework from 3,243 open-ended responses from 41 people who participated in 2020 and described what they thought sad expressors in 80 images were thinking, feeling, and/or intending to do. Face images were sourced from a novel set of naturalistic expressions (ANU Real Facial Expression Database), as well as a traditional posed expression database (Radboud Faces Database). The resultant framework revealed clear themes around the expressors' thoughts (e.g., acceptance, contemplation, disbelief), social needs (e.g., social support or withdrawal), social behaviours/intentions (e.g., mock or manipulate), and the precipitating events (e.g., social or romantic conflict). Expressions that were perceived as genuine were more frequently described as thinking deeply, reflecting, or feeling regretful, whereas those perceived as posed were more frequently described as exaggerated, overamplified, or dramatised. Overall, findings highlight that facial expressions — even with high levels of consensus about the emotion category they belong to — are interpreted in nuanced and complex ways that emphasise their role as other-oriented social tools, and convey semantically related emotion categories that share smooth gradients with one another. Our novel thematic framework also provides an important foundation for future work aimed at understanding variation in the social functions of sadness, including exploring potential differences in interpretations across cultural settings. [ABSTRACT FROM AUTHOR]

Although the same emotion can be related to different facial prototypes, little is known about the impact of differences in these prototypes on emotion perception. The present research aimed to investigate the impact of different facial prototypes on emotion perception, focusing specifically on anger and disgust. Four major prototypes of anger and six major prototypes of disgust by Asian and White targets were generated by FaceGen and MetaHuman programs. These prototypes were presented to participants from Canada and China, who were asked to identify the corresponding emotions. The results revealed that emotional signals conveyed by different facial prototypes were not uniform. While some prototypes cued distinctive emotions, others cued mixed emotions. Moreover, the signal value of specific prototypes varied across perceiver culture, with prototypes generally perceived as more mixed in Chinese compared to Canadian participants. Notably, emotional signals were also influenced by the facial generation software. Together these findings highlight the importance of considering the impact of specific facial prototypes on emotion perception, especially in cross-cultural contexts, and caution researchers to be mindful of the facial generation software when examining potential variability in emotional decoding associated with different facial prototypes. [ABSTRACT FROM AUTHOR]

To clinically evaluate previously developed pain scales [Donkey Chronic Pain Composite Pain Scale (DCP-CPS), Donkey Chronic Pain Facial Assessment of Pain (DCP-FAP) and combined Donkey Chronic Pain Scale (DCPS)], including behavioural and facial expression–based variables, for the assessment of chronic pain in donkeys. Prospective, blinded clinical study. A group of 77 donkeys (34 patients and 43 healthy control animals). Animals were assessed by two observers that were blinded to the condition of the animals. Both DCP-CPS and DCP-FAP, and resulting combined DCPS scores, showed good interobserver reliability [intraclass correlation coefficient (ICC) = 0.91, 95% confidence interval (CI) = 0.86–0.95, p < 0.001; ICC = 0.71, CI = 0.50–0.83, p < 0.001 and ICC = 0.84, CI = 0.72–0.91, p < 0.001, respectively]. All scores (DCP-CPS, DCP-FAP and the resulting combined DCPS) were significantly higher for patients than for controls at all time points (p < 0.001 for all three scales). Sensitivity and specificity for identification of pain (cut-off value >3) was 73.0% and 65.1% for DCP-CPS, and 60.9% and 83.3% for DCP-FAP, respectively. For the combined DCPS, sensitivity was 87.0% and specificity 90.9% (cut-off value >6). Based on behavioural and facial expression–based variables, DCPS proved a promising and reproducible tool to assess different types of chronic pain in donkeys. The combination of behavioural and facial expression–based variables showed the best discriminatory characteristics in the current study. Further studies are needed for refinement of these tools. [ABSTRACT FROM AUTHOR]

Introduction: Patients with chronic headaches and chronic oro‐facial pain commonly present psychosocial issues that can affect social interactions. A possible reason could be that patients with these disorders might present impairments in facial recognition, laterality judgement and also alexithymia. However, a systematic review summarizing the effects of facial emotion recognition, laterality judgement and alexithymia in individuals with headaches and oro‐facial pain is still not available. Aim: The main objective of this systematic review (SR) and meta‐analysis (MA) was to compile and synthesize the evidence on the occurrence of alexithymia, deficits in laterality or left–right (LR) recognition and/or facial emotion recognition (FER) in patients with chronic headache and facial pain. Methods: Electronic searches were conducted in five databases (up to September 2023) and a manual search to identify relevant studies. The outcomes of interest were alexithymia scores, speed and accuracy in LR and/or FER, or any other quantitative data assessing body image distortions. The screening process, data extraction, risk of bias and data analysis were performed by two independent assessors following standards for systematic reviews. Results: From 1395 manuscripts found, only 34 studies met the criteria. The overall quality/certainty of the evidence was very low. Although the results should be interpreted carefully, individuals with chronic headaches showed significantly higher levels of alexithymia when compared to healthy individuals. No conclusive results were found for the other variables of interest. Conclusion: Although the overall evidence from this review is very low, people with chronic primary headaches and oro‐facial pain could be regularly screened for alexithymia to guarantee appropriate management. [ABSTRACT FROM AUTHOR]

Facial expressions are among the earliest behaviors infants use to express emotional states, and are crucial to preverbal social interaction. Manual coding of infant facial expressions, however, is laborious and poses limitations to replicability. Recent developments in computer vision have advanced automated facial expression analyses in adults, providing reproducible results at lower time investment. Baby FaceReader 9 is commercially available software for automated measurement of infant facial expressions, but has received little validation. We compared Baby FaceReader 9 output to manual micro-coding of positive, negative, or neutral facial expressions in a longitudinal dataset of 58 infants at 4 and 8 months of age during naturalistic face-to-face interactions with the mother, father, and an unfamiliar adult. Baby FaceReader 9's global emotional valence formula yielded reasonable classification accuracy (AUC =.81) for discriminating manually coded positive from negative/neutral facial expressions; however, the discrimination of negative from neutral facial expressions was not reliable (AUC =.58). Automatically detected a priori action unit (AU) configurations for distinguishing positive from negative facial expressions based on existing literature were also not reliable. A parsimonious approach using only automatically detected smiling (AU12) yielded good performance for discriminating positive from negative/neutral facial expressions (AUC =.86). Likewise, automatically detected brow lowering (AU3+AU4) reliably distinguished neutral from negative facial expressions (AUC =.79). These results provide initial support for the use of selected automatically detected individual facial actions to index positive and negative affect in young infants, but shed doubt on the accuracy of complex a priori formulas. [ABSTRACT FROM AUTHOR]

Immediate lengthening temporalis myoplasty (Labbé procedure) for immediate dynamic facial reanimation after nerve-inclusive parotidectomy in the elderly population is undocumented in the literature. The aim of this work was to determine whether the Labbé approach could achieve immediate, good functional and static results in elderly patients with acquired facial palsy. A retrospective analysis of five patients with parotid malignancies involving the facial nerve who underwent parotidectomy and an immediate Labbé procedure was performed. The House–Brackmann and Sunnybrook scores for facial palsy were used as objective measurements of the functional outcome. All patients underwent total parotidectomy, neck dissection, Labbé procedure, immediate temporary tarsorrhaphy, brow lift, and postoperative radiotherapy. Mean patient age was 83 (range 73–87) years. The average resected tumour size was 3.54 cm. The mean duration of surgery was 324 min and length of hospital stay 4 days. All patients experienced an improvement in House–Brackmann of one grade postoperative (grade V to IV in four, grade VI to V in one); the Sunnybrook score improved by 31 points on average (mean preoperative 3.8 vs postoperative 34.8). An immediate Labbé procedure following ablative parotid malignancy resection is a reliable and safe reconstructive procedure in a carefully selected elderly population, providing acceptable immediate static and dynamic hemifacial mimetic function and eliminating an additional facial palsy correction procedure. [ABSTRACT FROM AUTHOR]

Many people including those with visual impairment and blindness take advantage of video conferencing tools to meet people. Video conferencing tools enable them to share facial expressions that are considered as one of the most important aspects of human communication. This study aims to advance knowledge of how those with visual impairment and blindness share their facial expressions of emotions virtually. This study invited a convenience sample of 28 adults with visual impairment and blindness to Zoom video conferencing. The participants were instructed to pose facial expressions of basic human emotions (anger, fear, disgust, happiness, surprise, neutrality, calmness, and sadness), which were video recorded. The facial expressions were analyzed using the Facial Action Coding System (FACS) that encodes the movement of specific facial muscles called Action Units (AUs). This study found that there was a particular set of AUs significantly engaged in expressing each emotion, except for sadness. Individual differences were also found in AUs influenced by the participants' visual acuity levels and emotional characteristics such as valence and arousal levels. The research findings are anticipated to serve as the foundation of knowledge, contributing to developing emotion-sensing technologies for those with visual impairment and blindness. [ABSTRACT FROM AUTHOR]

Despite the growing importance of facial expressions in online brand communications, little is known about the positive and negative effects of replacing human facial expressions with emojis. To address this gap, this research examines how facial expressions (emojis versus human faces) shape consumers' emotional contagion and brand fun. Findings from three experimental studies (two online and one with eye-tracking) demonstrate that the presence of emojis increases brand fun due to the underlying mechanism of emotional contagion. However, although emojis might foster positive brand outcomes, they reduce credibility compared to brand communications using human faces. Finally, this research provides relevant managerial implications for brands that wish to create communications using facial expressions since emojis can positively impact product engagement. [ABSTRACT FROM AUTHOR]

The necessity of identifying an individual's intentions solely through facial expressions, without verbal communication, is crucial in daily life. Various approaches employing Artificial Intelligence interventions are utilized to recognize emotions. Consequently, the present study aims to propose an optimal image processing approach named as Hybrid Optimization Based Artificial Neural Network (HOBANN) for the recognition of facial expression. In this process, the implementation of Adaptive Gabor filter is preferred to remove the noise contents in the input image while preserving edge information effectively. Following this, Cascaded Fuzzy C-Means (CFCM) approach is employed to segment the image into multiple sections, enhancing accuracy across an extensive range. In the process of feature extraction, an efficient method known as Gray-Level Co-occurrence Matrix (GLCM) is employed. This method offers several benefits, including reduced complexity and optimal computations of pixel variance. Additionally, a novel hybrid optimization technique called Artificial Bee Colony-Cuckoo Search (ABC-CS) is introduced. This hybrid optimization is applied in the final classification process using an Artificial Neural Network (ANN) classifier, aiming to recognize expressions with maximum accuracy effectively. The Matlab simulink is preferred to validate the entire study and the simulated outcomes have demonstrated that the overall performance of this approach is significantly high as it delivers optimal accuracy rate of 97.93%. [ABSTRACT FROM AUTHOR]

• SBR was suppressed by fear. • Fear increased SBR when a threat stimulus was present. • No change in SBR was detected in control conditions, and the findings were unrelated to habituation or attention. This study evaluated the isolated and combined effects of fear and PPS paradigms on SBR. The prospective study was conducted with healthy participants. After stimulation of the right median nerve at the wrist, bilateral recordings were randomized under the following conditions: First experiment (with the right hand on the chair armrest): i. baseline recordings, ii. while watching fearful facial expressions from the Karolinska Emotional Faces battery (fear), iii. post-watching (post-fear), iv. while watching neutral facial expressions from the same battery (neutral), v. Immediately after viewing (post-neutral). Second experiment (right hand 2 cm away from the right eye, PPS): i. reference condition (PPS), ii. while watching fearful facial expressions (PPS-fear), iii. while watching neutral facial expressions (PPS-neutral). In each condition, SBR latency, area, duration, and amplitudes were measured and compared between conditions. We included 16 participants. SBR could be recorded in 11 (mean age:30.7 ± 5.2, F/M:5/6). First experiment: SBR amplitude was significantly reduced in fear condition (p = 0.008), and SBR area was reduced considerably in fear and post-fear conditions (p = 0.004) compared to the baseline. Second experiment: The SBR area was higher in the PPS (p = 0.009) compared to the baseline and even higher in the fearPPS compared to the PPS (p = 0.038). In neutral or PPS-neutral conditions, the area of the SBR did not change significantly. Fear suppressed SBR, but fear increased SBR when a threat stimulus was present. The findings were unrelated to habituation or attention, indicating cortical-amygdala-bulbar connections. [ABSTRACT FROM AUTHOR]

Workflows are a standard and a crucial part of data-intensive research. They are iterative processes that support replicability and reproducibility by organising problem-solving tasks into well-documented sequential steps. Given the emergence of data-intensive design research, there is a need to adapt these practices in design research to help increase the transparency and, thus, quality of research. We present a step-by-step guide to applying a data analysis workflow in design research using an example of measuring empathic understanding via facial expression analysis. This note demonstrates the importance of a systematic workflow in data-intensive design research and how it adds the necessary transparency and detail to the method section. It shows how various research outcomes emerge throughout the process. Standardised data analysis, emphasising data preprocessing and exploration, and thorough reporting form the basis for research replicability, reproducibility, and transparency in data-intensive design research. [ABSTRACT FROM AUTHOR]

Previous studies have demonstrated that brain stimulation can alter an individual's physical appearance via dysregulation of themedial prefrontal cortex (MPFC). In this study, we attempted to determine if individuals who receive repetitive transcranial magnetic stimulation (rTMS) delivered to the MPFC were rated as more attractive by others. It has been previously reported that 1 hertz (Hz) (inhibitory) TMS can alter one's facial expressions such that frontal cortex inhibition can increase expressiveness. These alterations, detected by external observation, remain below the level of awareness of the subject itself. In Phase I, subjects (N = 10) received MPFC rTMS and had their photographs taken after each of the five stimulation conditions, in addition to making self-ratings across a number of variables, including attractiveness. In Phase II, participants (N = 430) rated five pictures of each of the Phase 1 individuals on attractiveness. It was found that there were no significant differences in self-assessment following rTMS (Phase I). However, attractiveness ratings differed significantly in Phase II. There was a significant difference found between 10Hz TMS delivered to the MPFC (p < 0.001), such that individuals were rated as less attractive. Furthermore, 1Hz TMS to the MPFC increased the number of 'Most Attractive' ratings, while 10Hz TMS decreased the number of 'Most Attractive' ratings (p < 0.001). These results suggest that the MPFC plays a role in attractiveness ratings to others. These data also support research showing that one's appearance can be altered below the level of awareness via rTMS. To our knowledge, this is the first investigation to examine how brain stimulation influences one's attractiveness. [ABSTRACT FROM AUTHOR]

Background: Attentional processes are influenced by both stimulus characteristics and individual factors such as mood or personal experience. Research has suggested that attentional biases to socially relevant stimuli may occur in individuals with a history of peer victimization in childhood and adolescence. Based on this, the present study aimed to examine attentional processes in response to emotional faces at both the behavioral and neurophysiological levels in participants with experiences of peer victimization. Methods: In a sample of 60 adult participants with varying severity of retrospectively reported peer victimization in childhood and adolescence, the dot-probe task was administered with angry, disgusted, sad, and happy facial expressions. In addition to behavioral responses, physiological responses (i.e., event-related potentials) were analyzed. Results: Analyses of mean P100 and P200 amplitudes revealed altered P200 amplitudes in individuals with higher degrees of peer victimization. Higher levels of relational peer victimization were associated with increased P200 amplitudes in response to facial expressions, particularly angry and disgusted facial expressions. Hierarchical regression analyses showed no evidence for an influence of peer victimization experiences on reaction times or P100 amplitudes in response to the different emotions. Conclusion: Cortical findings suggest that individuals with higher levels of peer victimization mobilize more attentional resources when confronted with negative emotional social stimuli. Peer victimization experiences in childhood and adolescence appear to influence cortical processes into adulthood. [ABSTRACT FROM AUTHOR]

Retrieving similar images from a dataset of images is always challenging for researchers, and it becomes more challenging under critical conditions like illumination variation and different facial expressions. Every image comprises three types of content, color, shape, and texture. Texture content of the image plays a vital role in the image retrieval process. Many researchers have been working on the image retrieval problem and have proposed many local descriptors for the last two decades. This study gives a comparative analysis of existing local descriptors using different facial image datasets. In recent trends, researchers retrieve similar images using deep learning techniques. This paper also discussed some of the existing Deep learning methods for retrieving similar images. [ABSTRACT FROM AUTHOR]

Mental illnesses such as depression are typically neurologically related psychological disorders that affect people’s mood, thinking and behavior. As the number of students who are concerned about their mental health continues to rise, depression has emerged as a mental health concern that has a significant impact on both students’ academic performance and overall lives. To identify depression in students at an earlier stage, the purpose of this study was to provide a potentially unique approach. An approach to the detection of mental illnesses that is based on deep learning networks is proposed in this paper. First, facial expression and physical activity data are utilized for detecting depression. Second, the transformer model is utilized to extract the characteristics of the individual’s physical behavior, and the multiregional attention network (MRAN) is utilized to extract the characteristics of the individual’s emotions. The information that is obtained from the two modalities is complementary. Finally, at the fusion stage, this work applies the classification prediction of depression and nondepression (normal) at the decision level. This is done to ensure that the respective modal properties that were learned by the two channels are preserved in their entirety. We have demonstrated that our strategy is highly effective by performing experimental validation using a dataset that we developed ourselves. It is possible to identify depression in children at an earlier stage with the help of this effective remedy. It is anticipated that the findings of this study will provide an efficient screening tool for depression to educational institutions and organizations that focus on mental health, hence assisting students in receiving essential assistance and intervention at an earlier stage. [ABSTRACT FROM AUTHOR]