Facial Emotion Research Articles

Sharing Culture and Management of Sex Work Information on Mobile Phones by Female Sex Workers in Nepal: Implications for mHealth Practice and Research

ABSTRACT Female sex workers (FSWs) in Nepal continue to be disproportionately at risk for Sexually Transmitted Infections (STIs), including HIV. Due to stigma related to sex work and HIV, FSWs keep their sex work information hidden, which poses a barrier to seeking health services. Emerging research indicates a high uptake of mobile phones among FSWs in Nepal. Mobile health (mHealth) interventions can provide health information and linkage to care. However, largely missing from the literature is FSWs’ experience of managing the information about their involvement in sex work in a culture where sharing personal information and belongings is a part of the social norm, and maintaining privacy could have negative social repercussions. The current study aims to understand how FSWs perceive and manage privacy when they share their mobile phones. Using the Communication Privacy Management theory, we explore FSWs’ perception of the threat to their privacy posed by mobile phones. We conducted 30 in-depth interviews among FSWs in Kathmandu, Nepal. Results showed that all participants owned mobile phones, and sharing devices was common. Mobile phones pose a considerable challenge in keeping sex work information private, and FSWs use various communication strategies to circumvent privacy threats. The findings highlight the mental and emotional burden FSWs face trying to conceal their private information in a sharing culture. The study discusses the importance of theorizing privacy in the cultural context of the Global South and the practical implications for developing mHealth interventions for this population.

Machine learning judged neutral facial expressions as key factors for a “good therapist” within the first five minutes: An experiment to simulate online video counselling

ObjectiveMachine learning models were employed to discern patients' impressions from the therapists' facial expressions during a virtual online video counselling session. MethodsEight therapists simulated an online video counselling session for the same patient. The facial emotions of the therapists were extracted from the session videos; we then utilized a random forest model to determine the therapist's impression as perceived by the patients. ResultsThe therapists' neutral facial expressions were important controlling factors for patients' impressions. A predictive model with three neutral facial features achieved an accuracy of 83% in identifying patients' impressions. ConclusionsNeutral facial expressions may contribute to patient impressions in an online video counselling environment with spatiotemporal disconnection. InnovationExpression recognition techniques were applied innovatively to an online counselling setting where therapists' expressions are limited. Our findings have the potential to enhance psychiatric clinical practice using Information and Communication Technology.

Efficient Facial Emotion Detection through Deep Learning Techniques

Efficient Facial Emotion Detection through Deep Learning Techniques

Parental depression moderates the relationship between childhood maltreatment and the recognition of children expressions of emotions.

Sensitivity plays a crucial role in parenting as it involves the ability to perceive and respond appropriately to children's signals. Childhood maltreatment and depression can negatively impact adults' ability to recognize emotions, but it is unclear which of these factors has a greater impact or how they interact. This knowledge is central to developing efficient, targeted interventions. This paper examines the interaction between parents' depressive symptoms and childhood maltreatment and its influence on their ability to recognize the five basic emotions (happiness, anger, sadness, fear, and disgust) in children's faces. The sample consisted of 52 parents. Depressive symptoms were measured by the depression subscale of the Brief Symptom Inventory-18 (BSI-18), and maltreatment history was assessed by the Childhood Trauma Questionnaire (CTQ). Children's emotional stimuli were morphed images created using The Child Affective Facial Expression (CAFE) database. Our findings indicate that depressive symptoms moderate the relationship between parents' history of childhood maltreatment and emotion recognition skills. Parents with higher depressive symptoms had lower emotion recognition accuracy when they had not experienced maltreatment. When childhood maltreatment was severe, emotion recognition skills were more consistent across all levels of depression. The relationship between depression and emotion recognition was primarily linked to recognizing sadness in children's faces. These findings highlight how different experiences can affect parental abilities in emotion recognition and emphasize the need for interventions tailored to individual profiles to improve their effectiveness.

Disentangling the relationship between social cognition, executive functions and behaviour changes in amyotrophic lateral sclerosis

BackgroundSocial cognition (SC) deficits are included in the amyotrophic lateral sclerosis-frontotemporal spectrum disorder (ALS-FTDS) revised diagnostic criteria. However, the impact of SC assessment on cognitive classification and the cognitive–behavioural correlates...

How movies move us - movie preferences are linked to differences in neuronal emotion processing of fear and anger: an fMRI study.

As a source of audio-visual stimulation, movies expose people to various emotions. Interestingly, several genres are characterized by negative emotional content. Albeit theoretical approaches exist, little is known about preferences for specific movie genres and the neuronal processing of negative emotions. We investigated associations between movie genre preference and limbic and reward-related brain reactivity to close this gap by employing an fMRI paradigm with negative emotional faces in 257 healthy participants. We compared the functional activity of the amygdala and the nucleus accumbens (NAcc) between individuals with a preference for a particular movie genre and those without such preference. Amygdala activation was relatively higher in individuals with action movie preference (p TFCE-FWE = 0.013). Comedy genre preference was associated with increased amygdala (p TFCE-FWE = 0.038) and NAcc activity (p TFCE-FWE = 0.011). In contrast, crime/thriller preference (amygdala: p TFCE-FWE ≤ 0.010, NAcc: p TFCE-FWE = 0.036), as well as documentary preference, was linked to the decreased amygdala (p TFCE-FWE = 0.012) and NAcc activity (p TFCE-FWE = 0.015). The study revealed associations between participants' genre preferences and brain reactivity to negative affective stimuli. Interestingly, preferences for genres with similar emotion profiles (action, crime/thriller) were associated with oppositely directed neural activity. Potential links between brain reactivity and susceptibility to different movie-related gratifications are discussed.

Simple Histogram Equalization Technique Improves Performance of VGG Models on Facial Emotion Recognition Datasets

Facial emotion recognition (FER) is crucial across psychology, neuroscience, computer vision, and machine learning due to the diversified and subjective nature of emotions, varying considerably across individuals, cultures, and contexts. This study explored FER through convolutional neural networks (CNNs) and Histogram Equalization techniques. It investigated the impact of histogram equalization, data augmentation, and various model optimization strategies on FER accuracy across different datasets like KDEF, CK+, and FER2013. Using pre-trained VGG architectures, such as VGG19 and VGG16, this study also examined the effectiveness of fine-tuning hyperparameters and implementing different learning rate schedulers. The evaluation encompassed diverse metrics including accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision–Recall Curve (AUC-PRC), and Weighted F1 score. Notably, the fine-tuned VGG architecture demonstrated a state-of-the-art performance compared to conventional transfer learning models and achieved 100%, 95.92%, and 69.65% on the CK+, KDEF, and FER2013 datasets, respectively.

Neuroticism/negative emotionality is associated with increased reactivity to uncertain threat in the bed nucleus of the stria terminalis, not the amygdala.

Neuroticism/Negative Emotionality (N/NE)-the tendency to experience anxiety, fear, and other negative emotions-is a fundamental dimension of temperament with profound consequences for health, wealth, and wellbeing. Elevated N/NE is associated with a panoply of adverse outcomes, from reduced socioeconomic attainment to psychiatric illness. Animal research suggests that N/NE reflects heightened reactivity to uncertain threat in the bed nucleus of the stria terminalis (BST) and central nucleus of the amygdala (Ce), but the relevance of these discoveries to humans has remained unclear. Here we used a novel combination of psychometric, psychophysiological, and neuroimaging approaches to rigorously test this hypothesis in an ethnoracially diverse, sex-balanced sample of 220 emerging adults selectively recruited to encompass a broad spectrum of N/NE. Cross-validated robust-regression analyses demonstrated that N/NE is preferentially associated with heightened BST activation during the uncertain anticipation of a genuinely distressing threat (aversive multimodal stimulation), whereas N/NE was unrelated to BST activation during certain-threat anticipation, Ce activation during either type of threat anticipation, or BST/Ce reactivity to threat-related faces. It is often assumed that different threat paradigms are interchangeable assays of individual differences in brain function, yet this has rarely been tested. Our results revealed negligible associations between BST/Ce reactivity to the anticipation of threat and the presentation of threat-related faces, indicating that the two tasks are non-fungible. These observations provide a framework for conceptualizing emotional traits and disorders; for guiding the design and interpretation of biobank and other neuroimaging studies of psychiatric risk, disease, and treatment; and for informing mechanistic research.

Effectiveness of Group-Play Training on Emotion Recognition for Young Children Who Are Deaf or Hard of Hearing

Abstract: Deaf and hard of hearing (DHH) children are developmentally delayed in facial emotion recognition (FER). This study aimed to explore the possibility of enhancing the FER ability of DHH preschoolers through a group-play intervention. Nineteen children with a cochlear implant or hearing aid were enrolled in a 4-week intervention; six DHH children were assigned to a control group. The training program included a learning procedure for four basic emotions: happiness, sadness, anger, and fear. A pretest/posttest design was used to measure the DHH children's FER performance. The results indicated that although the two groups performed comparably on the FER task before the training (pretest), the performance of the intervention group was significantly better than that of the control group in the posttest. Moreover, the DHH children in the intervention group showed the greatest improvement in FER of happiness.

Effects of Intervention Strategies-Based on Positive Psychology on the Emotional Cognition, Mental Health, and Recovery of Speech Function in Speech Disabilities.

Compared with able-bodied people, speech disabilities are more prone to various mental health problems. We aimed to explore the impact of positive psychology-based intervention strategies on emotional cognition, mental health, and recovery of speech function in speech disabilities. In May 2023, 306 cases of speech disabilities were selected from 112 village committees and 129 neighborhood committees in Jingmen City, China. The control group was given routine speech rehabilitation training, and the observation group was given an intervention strategies-based on positive psychology based on the above training. The Symptom Checklist-90 (SCL-90), Chinese Facial Emotion Test (CFET), Comprehensive Function Assessment for Disabled Children (CFADC), and Boston Diagnostic Aphasia Examination (BDAE) were used to evaluate the two groups of patients before and after intervention. After the intervention, the mental state scores (psychotic, obsessive-compulsive symptoms, somatization, paranoia, terror, hostility, anxiety, and depression) of the observation group were lower than those of the control group (P<0.05). The correct emotional scores in the observation group were higher than those in the control group were. However, the remote error scores of the observation group were lower than those of the control group were. The difference was also statistically significant (P<0.05). The cognitive function score, speech function score, and BDAE score (retelling, writing, fluency, and reading comprehension) of the observation group were all higher than those of the control group (P<0.05). The intervention strategies-based on positive psychology could promote the improvement of health problems and speech function in speech disabilities.

Low-resolution facial emotion recognition on low-cost devices

The low-resolution input image is a crucial challenge for applying facial emotion recognition in real-world scenarios. The critical problem is that valuable object features are relatively lost in the extraction process due to their small size. On the other hand, this vision system is required by a machine to run smoothly on low-cost devices. Facial emotion recognition using a lightweight feature extractor is proposed in this study to effectively capture crucial facial components in a low-resolution image. To compromise the running speed, this work offers an efficient feature convolution to discriminate specific facial features. In addition, the system is embedded with an attentive module to capture important features and correlate them. Our model performance is evaluated on low-resolution public datasets achieving the accuracy of 97.34\%, 81.10\%, and 80.12\% on KDEF, RFDB, and FER-plus, respectively. The practical application demands that the deep learning model can operate fast on inexpensive devices. Consequently, the model achieved a speed of 290 FPS on a CPU device.

Facial emotion recognition and judgment of affective scenes in Parkinson's disease

Emotional dysfunctions in Parkinson's disease (PD) remain a controversial issue. While previous investigations showed compromised recognition of expressive faces in PD, no studies evaluated potential deficits in recognizing the emotional valence of affective scenes. This study aimed to investigate both facial emotion recognition performance and the ability to judge affective scenes in PD patients.Forty PD patients (mean age ± SD: 64.50 ± 8.19 years; 27 men) and forty healthy subjects (64.95 ± 8.25 years; 27 men) were included. Exclusion criteria were previous psychiatric disorders, previous Deep Brain Stimulation, and cognitive impairment. Participants were evaluated through the Ekman 60-Faces test and the International Affective Picture System. The accuracy in recognizing the emotional valence of facial expressions and affective scenes was compared between groups using linear mixed models. Pearson's correlation was performed to test the association between accuracy measures.The groups did not differ in sex, age, education, and Mini-Mental State Examination scores. Patients showed a lower recognition accuracy of facial expressions (68.54 % ± 15.83 %) than healthy participants (78.67 % ± 12.04 %; p < 0.001). Specifically, the PD group was characterized by lower recognition of faces expressing fear, sadness, and anger than the control group (all p < 0.020). No difference was detected for faces expressing disgust, surprise, and happiness (all p ≥ 0.25). Furthermore, patients showed lower accuracy in recognizing the emotional valence of affective scenes (66.75 % ± 14.59 %) than healthy subjects (74.83 % ± 12.65 %; p = 0.010). Pearson's correlations indicated that higher accuracy in recognizing the emotional facial expressions was associated with higher accuracy in classifying the valence of affective scenes in patients (r = 0.57, p < 0.001) and control participants (r = 0.57, p < 0.001).Our study suggested maladaptive affective processing in PD, leading patients to misinterpret both facial expressions and the emotional valence of complex evocative scenes.

An Approach for Evaluation and Recognition of Facial Emotions Using EMG Signal

Background: Facial electromyography (fEMG) records muscular activities from the facial muscles, which provides details regarding facial muscle stimulation patterns in experimentation. Objectives: The Principal Component Analysis (PCA) is mostly implemented, whereas the actual or unprocessed initial fEMG data are rendered into low-spatial units with minimizing the level of data repetition. Methods: Facial EMG signal was acquired by using the instrument BIOPAC MP150. Four electrodes were fixed on the face of each participant for capturing the four different emotions like happiness, anger, sad and fear. Two electrodes were placed on arm for grounding purposes. Results: The aim of this research paper is to propagate the functioning of PCA in synchrony with the subjective fEMG analysis and to give a thorough apprehension of the advanced PCA in the areas of machine learning. It describes its arithmetical characteristics, while PCA is estimated by implying the covariance matrix. Datasets which are larger in size are progressively universal and their interpretation often becomes complex or tough. So, it is necessary to minimize the number of variables and elucidate linear compositions of the data to explicate it on a huge number of variables with a relevant approach. Therefore, Principal Component Analysis (PCA) is applied because it is an unsupervised training method that utilizes advanced statistical concept to minimize the dimensionality of huge datasets. Conclusion: This work is furthermore inclined toward the analysis of fEMG signals acquired for four different facial expressions using Analysis of Variance (ANOVA) to provide clarity on the variation of features.

Transfer Learning for Facial Emotion Recognition on Small Datasets

In the context of human interactions, complexity science (CS) provides a way to understand the dynamics that arise from the interplay of different individuals. Recently, the possibility of applying the theory of CS to computer science, has shifted the focus to the research of machine learning methods for studying human behaviors and relational dynamics. Among the various existing AI techniques, facial emotion recognition (FER) has proven to be the best-performing and easiest to use human- computer interaction (HCI) tool for emotion detection. Despite the numerous existing approaches, the task of FER is not trivial, mainly due to the absence of large enough datasets to train deep learning (DL) models. A widely used solution is transfer learning (TL), which allows a model, pre-trained on enough data, to be used for a specific task where there is much less data available. The aim of our work is to test the effectiveness of TL for FER on an extremely small dataset, to understand which parameters need to be optimised to obtain the best outcomes. The results showed an overall accuracy of 85.54% for our model and revealed the concrete possibility of applying computer science to complex systems typical of the human psyche.

Deciphering Crime Suspects' Emotion for Investigative Precision using Deep Learning

Using Convolutional Neural Networks (CNNs) for Facial Emotion Recognition (FER) in criminal investigations is the main goal of the proposed study. Using cutting-edge CNN architectures trained on a variety of datasets to precisely identify emotions including joy, anger, fear, surprise, disgust, and sadness is one of the main components. The research seeks to improve the effectiveness of investigations by giving law enforcement a tool to interpret the subtle emotional cues in the facial expressions of potential suspects in crimes. The technique has potential uses in human-computer interface, security, and surveillance in addition to criminal investigations. The larger significance is in helping to create more precise and effective investigative procedures, which will ultimately promote a society that is safer and more secure. The study focuses on providing a brief overview, with a suggested method utilizing CNNs to trace crime suspects.

Emotion and anxiety interact to bias spatial attention.

Emotional expressions are an evolutionarily conserved means of social communication essential for social interactions. It is important to understand how anxious individuals perceive their social environments, including emotional expressions, especially with the rising prevalence of anxiety during the COVID-19 pandemic. Anxiety is often associated with an attentional bias for threat-related stimuli, such as angry faces. Yet the mechanisms by which anxiety enhances or impairs two key components of spatial attention-attentional capture and attentional disengagement-to emotional expressions are still unclear. Moreover, positive valence is often ignored in studies of threat-related attention and anxiety, despite the high occurrence of happy faces during everyday social interaction. Here, we investigated the relationship between anxiety, emotional valence, and spatial attention in 574 participants across two preregistered studies (data collected in 2021 and 2022; Experiment 1: n = 154, 54.5% male, Mage = 43.5 years; Experiment 2: n = 420, 58% male, Mage = 36.46 years). We found that happy faces capture attention more quickly than angry faces during the visual search experiment and found delayed disengagement from both angry and happy faces over neutral faces during the spatial cueing experiment. We also show that anxiety has a distinct impact on both attentional capture and disengagement of emotional faces. Together, our findings highlight the role of positively valenced stimuli in attracting and holding attention and suggest that anxiety is a critical factor in modulating spatial attention to emotional stimuli. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Approach-avoidance tendencies in proactive and reactive aggression.

Aggressive behaviors have been related to approach/avoidance tendencies. In our current study, we investigated whether approach/avoidance tendencies for angry versus fearful emotional expressions were differentially predictive of children's reactive and proactive aggression. A total of 116 children (58 girls, Mage = 10.90, standard deviation SDage = 0.98) completed an approach/avoidance task (AAT) and a stimulus-response compatibility task (SRCT), both measuring the extent to which they tended to approach or avoid angry and fearful facial expressions relative to neutral facial expressions. Children also completed a self-report scale of reactive and proactive aggression. Although none of the approach/avoidance tendency scores correlated significantly with either of the aggression scores, stronger approach tendencies for angry faces and stronger avoidance tendencies for fearful faces in the AAT predicted more reactive aggression. Similar yet nonsignificant results were found for proactive aggression, but no effects were replicated in the SRCT. Our results thus invite the conclusion that reactive aggression is characterized by a tendency to approach angry faces and a tendency to avoid fearful faces. However, the poor discrimination between both types of aggression as well as the lack of convergence between the results of our two measures of approach/avoidance tendencies indicates that further research is needed to establish the role of approach/avoidance tendencies for emotional faces as markers for childhood aggression.

Eye-tracking measurement of attention bias to social threat among youth: A replication and extension study

Attentional bias to social threat cues has been linked to heightened anxiety and irritability in youth. Yet, inconsistent methodology has limited replication and led to mixed findings. The current study aims to 1) replicate and extend two previous pediatric studies demonstrating a relationship between negative affectivity and attentional bias to social threat and 2) examine the test-retest reliability of an eye-tracking paradigm among a subsample of youth. Attention allocation to negative versus non-negative emotional faces was measured using a free-viewing eye-tracking task among youth (N = 185 total, 60 % female, Mage = 13.10 years, SDage = 2.77) with three face-pair conditions: happy-angry, neutral-disgust, sad-happy. Replicating procedures of two previous studies, linear mixed-effects models compared attention bias between children with anxiety disorders and healthy controls. Bifactor analysis was used to parse shared versus unique facets of general negative affectivity (i.e., anxiety, irritability), which were then examined in relation to attention bias. Test-retest reliability of the bias-index was estimated among a subsample of youth (N = 36). No significant differences in attention allocation or bias emerged between anxiety and healthy control groups. While general negative affectivity across the sample was not associated with attention bias, there was a positive relationship for anxiety and irritability on duration of attention allocation toward negative faces. Test-retest reliability for attention bias was moderate (r = 0.50, p < .01). While anxiety-related findings from the two previous studies were not replicated, the relationship between attention bias and facets of negative affect suggests a potential target for treatment. Evidence for test-retest reliability encourages future use of the eye-tracking task for researchers.

Oral Oxytocin Blurs Sex Differences in Amygdala Responses to Emotional Scenes

BackgroundSex differences are shaped both by innate biological differences and the social environment and are frequently observed in human emotional neural responses. Oral administration of oxytocin (OXT), as an alternative and noninvasive intake method, has been shown to produce sex-dependent effects on emotional face processing. However, it is unclear whether oral OXT produces similar sex-dependent effects on processing continuous emotional scenes. MethodsThe current randomized, double-blind, placebo-controlled neuropsychopharmacological functional magnetic resonance imaging experiment was conducted in 147 healthy participants (OXT = 74, men/women = 37/37; placebo = 73, men/women = 36/37) to examine the oral OXT effect on plasma OXT concentrations and neural response to emotional scenes in both sexes. ResultsAt the neuroendocrine level, women showed lower endogenous OXT concentrations than men, but oral OXT increased OXT concentrations equally in both sexes. Regarding neural activity, emotional scenes evoked opposite valence-independent effects on right amygdala activation (women > men) and its functional connectivity with the insula (men > women) in men and women in the placebo group. This sex difference was either attenuated (amygdala response) or even completely eliminated (amygdala-insula functional connectivity) in the OXT group. Multivariate pattern analysis confirmed these findings by developing an accurate sex-predictive neural pattern that included the amygdala and the insula under the placebo but not the OXT condition. ConclusionsThe results of the current study suggest a pronounced sex difference in neural responses to emotional scenes that was eliminated by oral OXT, with OXT having opposite modulatory effects in men and women. This may reflect oral OXT enhancing emotional regulation to continuous emotional stimuli in both sexes by facilitating appropriate changes in sex-specific amygdala-insula circuitry.

Transfer Learning Based Face Emotion Recognition Using Meshed Faces and Oval Cropping: A Novel Approach

Transfer Learning Based Face Emotion Recognition Using Meshed Faces and Oval Cropping: A Novel Approach