Search Results (340)

Stress in pigs poses significant challenges to animal welfare and productivity in modern pig farming, contributing to increased antimicrobial use and the rise of antimicrobial resistance (AMR). This study involves stress classification in pregnant sows by exploring five deep learning models: ConvNeXt, EfficientNet_V2, MobileNet_V3, RegNet, and Vision Transformer (ViT). These models are used for stress detection from facial images, leveraging an expanded dataset. A facial image dataset of sows was collected at Scotland’s Rural College (SRUC) and the images were categorized into primiparous Low-Stressed (LS) and High-Stress (HS) groups based on expert behavioural assessments and cortisol level analysis. The selected deep learning models were then trained on this enriched dataset and their performance was evaluated using cross-validation on unseen data. The Vision Transformer (ViT) model outperformed the others across the dataset of annotated facial images, achieving an average accuracy of 0.75, an F1 score of 0.78 for high-stress detection, and consistent batch-level performance (up to 0.88 F1 score). These findings highlight the efficacy of transformer-based models for automated stress detection in sows, supporting early intervention strategies to enhance welfare, optimize productivity, and mitigate AMR risks in livestock production. Full article

Background/Objectives: Successful discourse relies not only on linguistic but also on prosodic information. Difficulty recognizing emotion conveyed through prosody (receptive affective aprosodia) following right hemisphere stroke (RHS) significantly disrupts communication participation and personal relationships. Growing evidence suggests that damage to white matter in addition to gray matter structures impairs affective prosody recognition. The current study investigates lesion–symptom associations in receptive affective aprosodia during RHS recovery by assessing whether disruptions in distinct white matter structures impact different underlying affective prosody recognition skills. Methods: Twenty-eight adults with RHS underwent neuroimaging and behavioral testing at acute, subacute, and chronic timepoints. Fifty-seven healthy matched controls completed the same behavioral testing, which comprised tasks targeting affective prosody recognition and underlying perceptual, cognitive, and linguistic skills. Linear mixed-effects models and multivariable linear regression were used to assess behavioral performance recovery and lesion–symptom associations. Results: Controls outperformed RHS participants on behavioral tasks earlier in recovery, and RHS participants’ affective prosody recognition significantly improved from acute to chronic testing. Affective prosody and emotional facial expression recognition were affected by external capsule and inferior fronto-occipital fasciculus lesions while sagittal stratum lesions impacted prosodic feature recognition. Accessing semantic representations of emotions implicated the superior longitudinal fasciculus. Conclusions: These findings replicate previously observed associations between right white matter tracts and affective prosody recognition and further identify lesion–symptom associations of underlying prosodic recognition skills throughout recovery. Investigation into prosody’s behavioral components and how they are affected by injury can help further intervention development and planning. Full article

Emotion recognition is an essential social ability that continues to develop across adolescence, a period of critical socio-emotional changes. In the present study, we examine how signals from different sensory modalities, specifically touch and facial expressions, are integrated into a holistic understanding of another’s feelings. Adolescents (n = 30) and young adults (n = 30) were presented with dynamic faces displaying either a positive (happy) or a negative (sad) expression. Crucially, facial expressions were anticipated by a tactile stimulation, either positive or negative. Across two experiments, we use different tactile primes, both in first-person experience (experiment 1) and in the vicarious experience of touch (experiment 2). We measured accuracy and reaction times to investigate whether tactile stimuli affect facial emotional processing. In both experiments, results indicate that adolescents were more sensitive than adults to the influence of tactile primes, suggesting that sensory cues modulate adolescents’ accuracy and velocity in evaluating emotion facial expression. The present findings offer valuable insights into how tactile experiences might shape and support emotional development and interpersonal social interactions. Full article

Background/Objectives: Deficits in emotion recognition abilities have been described as risk factors for intimate partner violence (IPV) perpetration. However, much of this research is based on self-reports or instruments that present limited psychometric properties. While current scientific literature supports the use of eye tracking to assess cognitive and emotional processes, including emotional decoding abilities, there is a gap in the scientific literature when it comes to measuring these processes in IPV perpetrators using eye tracking in an emotional decoding task. Hence, the aim of this study was to examine the association between fixation times via eye tracking and emotional decoding abilities in IPV perpetrators, controlling for potential confounding variables. Methods: To this end, an emotion recognition task was created using an eye tracker in a group of 52 IPV perpetrators. This task consisted of 20 images with people expressing different emotions. For each picture, the facial region was selected as an area of interest (AOI). The fixation times were added to obtain a total gaze fixation time score. Additionally, an ad hoc emotional decoding multiple-choice test about each picture was developed. These instruments were complemented with other self-reports previously designed to measure emotion decoding abilities. Results: The results showed that the longer the total fixation times on the AOI, the better the emotional decoding abilities in IPV perpetrators. Specifically, fixation times explained 20% of the variance in emotional decoding test scores. Additionally, our ad hoc emotional decoding test was significantly correlated with previously designed emotion recognition tools and showed similar reliability to the eyes test. Conclusions: Overall, this pilot study highlights the importance of including eye movement signals to explore attentional processes involved in emotion recognition abilities in IPV perpetrators. This would allow us to adequately specify the therapeutic needs of IPV perpetrators to improve current interventions. Full article

Background/Objectives: For those with comorbid mild traumatic brain injury/post-traumatic stress disorder (mTBI/PTSD), deficits are common with regard to recognition of emotion expression in others. These deficits can cause isolation and suicidal ideation. For mTBI/PTSD, there is a dearth of information regarding effective treatment. In pilot work, we developed and tested an innovative treatment to improve recognition of both affect (facial expression of emotion) and prosody (spoken expression of emotion). Methods: We enrolled eight Veterans with mTBI/PTSD and administered eight treatment sessions. Measures included the following: Florida Affect Battery (FAB), a test of emotion recognition of facial affect and spoken prosody; Attention Index of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS); and Emotion Recognition Test (ERT), a speed test of facial emotion recognition. Results: There was a significant treatment response according to the FAB (p = 0.01, effect size = 1.2); RBANS attention index (p = 0.04, effect size = 0.99); and trending toward significance for the ERT (0.17, effect size 0.75). Participants were able to engage actively in all eight sessions and provided qualitative evidence supporting generalization of the training to interpersonal relationships. Conclusions: Our data show promising clinical potential and warrant future research, given the importance of developing novel interventions to train and restore recognition of emotion in Veterans with mTBI/PTSD. Full article

Neuro-Affective Intelligence (NAI) integrates neuroscience, psychology, and artificial intelligence to support neurodivergent children through personalized Child–Machine Interaction (CMI). This paper presents an adaptive neuro-affective system designed to enhance engagement in children with neurodevelopmental disorders through serious games. The proposed framework incorporates real-time biophysical signals—including EEG-based concentration, facial expressions, and in-game performance—to compute a personalized engagement score. We introduce a novel mechanism, Bayesian Immediate Feedback Learning (BIFL), which dynamically selects visual, auditory, or textual stimuli based on real-time neuro-affective feedback. A multimodal CNN-based classifier detects mental states, while a probabilistic ensemble merges affective state classifications derived from facial expressions. A multimodal weighted engagement function continuously updates stimulus–response expectations. The system adapts in real time by selecting the most appropriate cue to support the child’s cognitive and emotional state. Experimental validation with 40 children (ages 6–10) diagnosed with Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) demonstrates the system’s effectiveness in sustaining attention, improving emotional regulation, and increasing overall game engagement. The proposed framework—combining neuro-affective state recognition, multimodal engagement scoring, and BIFL—significantly improved cognitive and emotional outcomes: concentration increased by 22.4%, emotional engagement by 24.8%, and game performance by 32.1%. Statistical analysis confirmed the significance of these improvements ($p<0.001$, Cohen’s $d>1.4$). These findings demonstrate the feasibility and impact of probabilistic, multimodal, and neuro-adaptive AI systems in therapeutic and educational applications. Full article

Humor is widely recognized for its positive effects on well-being, including stress reduction, mood enhancement, and cognitive benefits. Yet, the lack of reliable tools to objectively quantify amusement—particularly its temporal dynamics—has limited progress in this area. Existing measures often rely on self-report or coarse summary ratings, providing little insight into how amusement unfolds over time. To address this gap, we developed a Random Forest model to predict the intensity of amusement evoked by humorous video clips, based on participants’ facial expressions—particularly the co-activation of Facial Action Units 6 and 12 (“% Smile”)—and video features such as motion, saliency, and topic. Our results show that exposure to humorous content significantly increases “% Smile”, with amusement peaking toward the end of videos. Importantly, we observed emotional carry-over effects, suggesting that consecutive humorous stimuli can sustain or amplify positive emotional responses. Even when trained solely on humorous content, the model reliably predicted amusement intensity, underscoring the robustness of our approach. Overall, this study provides a novel, objective method to track amusement on a fine temporal scale, advancing the measurement of nonverbal emotional expression. These findings may inform the design of emotion-aware applications and humor-based therapeutic interventions to promote well-being and emotional health. Full article

Facial expression recognition (FER) plays a critical role in affective computing, enabling machines to interpret human emotions through facial cues. While recent deep learning models have achieved progress, many still fail under real-world conditions such as occlusion, lighting variation, and subtle expressions. In this work, we propose FERONet, a novel hyper-attentive multimodal transformer architecture tailored for robust and real-time FER. FERONet integrates a triple-attention mechanism (spatial, channel, and cross-patch), a hierarchical transformer with token merging for computational efficiency, and a temporal cross-attention decoder to model emotional dynamics in video sequences. The model fuses RGB, optical flow, and depth/landmark inputs, enhancing resilience to environmental variation. Experimental evaluations across five standard FER datasets—FER-2013, RAF-DB, CK+, BU-3DFE, and AFEW—show that FERONet achieves superior recognition accuracy (up to 97.3%) and real-time inference speeds (<16 ms per frame), outperforming prior state-of-the-art models. The results confirm the model’s suitability for deployment in applications such as intelligent tutoring, driver monitoring, and clinical emotion assessment. Full article

Background. Emotions are a crucial part of our human nature. The recognition of emotions is an essential component of our social and emotional skills. Facial expressions serve as a key element in discerning others’ emotions. Different databases of images of facial emotion expressions exist worldwide; however, most of them are limited to only adult faces and include only the six basic emotions, as well as neutral faces, ignoring more complex emotional expressions. Here, we present the Children’s Emotions Database (CED), a novel repository featuring both basic and complex facial expressions captured from preschool-aged children. The CED is one of the first databases to include complex emotional expressions in preschoolers. Our aim was to develop such a database that can be used further for research and applied purposes. Methods. Three 6-year-old children (one female) were photographed while showing different facial emotional expressions. The photos were taken under standardized conditions. The children were instructed to express each of the following basic emotions: happiness, pleasant surprise, sadness, fear, anger, disgust; a neutral face; and four complex emotions: pride, guilt, compassion, and shame; this resulted in a total of eleven expressions for each child. Two photos per child were reviewed and selected for validation. The photo validation was performed with a sample of 104 adult raters (94 females; aged 19–70 years; M = 29.9; SD = 11.40) and a limited sample of 32 children at preschool age (17 girls; aged 4–7 years; M = 6.5; SD = 0.81). The validation consisted of two tasks—free emotion labeling and emotion recognition (with predefined labels). Recognition accuracy for each expression was calculated. Results and Conclusions. While basic emotions and neutral expressions were recognized with high accuracy, complex emotions were less accurately identified, consistent with the existing literature on the developmental challenges in recognizing such emotions. The current work is a promising new database of preschoolers’ facial expressions consisting of both basic and complex emotions. This database offers a valuable resource for advancing research in emotional development, educational interventions, and clinical applications tailored to early childhood. Full article

Facial expression recognition (FER) is a core technology that enables computers to understand and react to human emotions. In particular, the use of face alignment algorithms as a preprocessing step in image-based FER is important for accurately normalizing face images in terms of scale, rotation, and translation to improve FER accuracy. Recently, FER studies have been actively leveraging feature maps computed by face alignment networks to enhance FER performance. However, previous studies were limited in their ability to effectively apply information from specific facial regions that are important for FER, as they either only used facial landmarks during the preprocessing step or relied solely on the feature maps from the face alignment networks. In this paper, we propose the use of Keypoint Features extracted from feature maps at the coordinates of facial landmarks. To effectively utilize Keypoint Features, we further propose a Keypoint Feature regularization method using landmark perturbation for robustness, and an attention mechanism that emphasizes all Keypoint Features using representative Keypoint Features derived from a nasal base landmark, which carries information for the whole face, to improve performance. We performed experiments on the AffectNet, RAF-DB, and FERPlus datasets using a simply designed network to validate the effectiveness of the proposed method. As a result, the proposed method achieved a performance of 68.17% on AffectNet-7, 64.87% on AffectNet-8, 93.16% on RAF-DB, and 91.44% on FERPlus. Furthermore, the network pretrained on AffectNet-8 had improved performances of 94.04% on RAF-DB and 91.66% on FERPlus. These results demonstrate that the proposed Keypoint Features can achieve comparable results to those of the existing methods, highlighting their potential for enhancing FER performance through the effective utilization of key facial region features. Full article

This paper presents a real-time system for the detection and classification of facial micro-expressions, evaluated on the CASME II dataset. Micro-expressions are brief and subtle indicators of genuine emotions, posing significant challenges for automatic recognition due to their low intensity, short duration, and inter-subject variability. To address these challenges, the proposed system integrates advanced computer vision techniques, rule-based classification grounded in the Facial Action Coding System, and artificial intelligence components. The architecture employs MediaPipe for facial landmark tracking and action unit extraction, expert rules to resolve common emotional confusions, and deep learning modules for optimized classification. Experimental validation demonstrated a classification accuracy of 93.30% on CASME II, highlighting the effectiveness of the hybrid design. The system also incorporates mechanisms for amplifying weak signals and adapting to new subjects through continuous knowledge updates. These results confirm the advantages of combining domain expertise with AI-driven reasoning to improve micro-expression recognition. The proposed methodology has practical implications for various fields, including clinical psychology, security, marketing, and human-computer interaction, where the accurate interpretation of emotional micro-signals is essential. Full article

Accurate recognition of student emotions in smart classrooms is vital for understanding learning states. Visible light-based facial expression recognition is often affected by illumination changes, making thermal infrared imaging a promising alternative due to its robust temperature distribution symmetry. This paper proposes CTIFERK, a thermal infrared facial expression recognition model integrating Kolmogorov–Arnold Networks (KANs). By incorporating multiple KAN layers, CTIFERK enhances feature extraction and fitting capabilities. It also balances pooling layer information from the MobileViT backbone to preserve symmetrical facial features, improving recognition accuracy. Experiments on the Tufts Face Database, the IRIS Database, and the self-constructed GUET thermalface dataset show that CTIFERK achieves accuracies of 81.82%, 82.19%, and 65.22%, respectively, outperforming baseline models. These results validate CTIFERK’s effectiveness and superiority for thermal infrared expression recognition in smart classrooms, enabling reliable emotion monitoring. Full article

The deployment of emotion-recognition technologies expands across healthcare education and gaming sectors to improve human–computer interaction. These systems examine facial expressions together with vocal tone and physiological signals, which include pupil size and electroencephalogram (EEG), to detect emotional states and deliver customized responses. The technology provides benefits through accessibility, responsiveness, and adaptability but generates multiple complex ethical issues. The combination of emotional profiling with biased algorithmic interpretations of culturally diverse expressions and affective data collection without meaningful consent presents major ethical concerns. The increased presence of these systems in classrooms, therapy sessions, and personal devices makes the potential for misuse or misinterpretation more critical. The paper integrates findings from literature review and initial emotion-recognition studies to create a conceptual framework that prioritizes data dignity, algorithmic accountability, and user agency and presents a conceptual framework that addresses these risks and includes safeguards for participants’ emotional well-being. The framework introduces structural safeguards which include data minimization, adaptive consent mechanisms, and transparent model logic as a more complete solution than privacy or fairness approaches. The authors present functional recommendations that guide developers to create ethically robust systems that match user principles and regulatory requirements. The development of real-time feedback loops for user awareness should be combined with clear disclosures about data use and participatory design practices. The successful oversight of these systems requires interdisciplinary work between researchers, policymakers, designers, and ethicists. The paper provides practical ethical recommendations for developing affective computing systems that advance the field while maintaining responsible deployment and governance in academic research and industry settings. The findings hold particular importance for high-stakes applications including healthcare, education, and workplace monitoring systems that use emotion-recognition technology. Full article

In the research on intelligent perception, dynamic emotion recognition has been the focus in recent years. Small samples and unbalanced data are the main reasons for the low recognition accuracy of current technologies. Inspired by circular convolution networks, this paper innovatively proposes an adaptive dynamic micro-expression recognition algorithm based on self-supervised learning, namely MADV-Net. Firstly, a basic model is pre-trained with accurate tag data, and then an efficient facial motion encoder is used to embed facial coding unit tags. Finally, a cascaded pyramid structure is constructed by the multi-level adaptive dynamic encoder, and the multi-level head perceptron is used as the input into the classification loss function to calculate facial micro-motion features in the dynamic video stream. In this study, a large number of experiments were carried out on the open-source datasets SMIC, CASME-II, CAS(ME)^2, and SAMM. Compared with the 13 mainstream SOTA methods, the average recognition accuracy of MADV-Net is 72.87%, 89.94%, 83.32% and 89.53%, respectively. The stable generalization ability of this method is proven, providing a new research paradigm for automatic emotion recognition. Full article

While deep neural networks demonstrate robust performance in visual tasks, the long-tail distribution of real-world data leads to significant recognition accuracy degradation in critical scenarios such as medical human–robot affective interaction, particularly the misidentification of low-frequency negative emotions (e.g., fear and disgust) that may trigger psychological resistance in patients. Here, we propose a method based on dynamic intra-class clustering (DICC) to optimize the class imbalance problem in facial expression recognition tasks. The DICC method dynamically adjusts the distribution of majority classes by clustering them into subclasses and generating pseudo-labels, which helps the model learn more discriminative features and improve classification accuracy. By comparing with existing methods, we demonstrate that the DICC method can help the model achieve superior performance across various facial expression datasets. In this study, we conducted an in-depth evaluation of the DICC method against baseline methods using the FER2013, MMAFEDB, and Emotion-Domestic datasets, achieving improvements in classification accuracy of 1.73%, 1.97%, and 5.48%, respectively. This indicates that the DICC method can effectively enhance classification precision, especially in the recognition of minority class samples. This approach provides a novel perspective for addressing the class imbalance challenge in facial expression recognition and offers a reference for future research and applications in related fields. Full article