Facial Emotion Research Articles

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

Use of Reverse Inference in High Noise Stimuli to Investigate Bias Efficiencies of Face Emotion Processing in Major Depressive Disorder

Use of Reverse Inference in High Noise Stimuli to Investigate Bias Efficiencies of Face Emotion Processing in Major Depressive Disorder

STRESS DETECTION IN IT PROFESSIONALS

The goal of this project is to use image processing and machine learning to recognize stress in IT professionals. The aim is to monitor the mental health of people working in front of computers for long periods of time, control and reduce stress, and provide a better working environment for IT workers. This approach focuses on managing stress and creating a healthy work environment so employees can get the best out of themselves at work. The overall aim of the research is to propose a reliable, simple and accurate search. The main purpose of this study is to analyze and provide information about the stress of employees by estimating the stress of the individual according to the symptoms determined by the care. The main goal of our study is to use effective machine learning and imaging techniques to diagnose stress in IT professionals. Our system is an updated version of the original stress testing method that does not include real-time testing and in-person interviews, but does include real-time measurement and regular employee reviews and controls over time. Keep track of information/stress on body and mind by giving regular questions to check stress treatment. Our system mainly focuses on managing stress and creating a healthy working environment so that employees can make the most of stress during work. Key Words: Face Detection, Facial Emotions, Morphological Processing, Convolutional Neural Network.

Automatic detection of facial expressions during the Cyberball paradigm in Borderline Personality Disorder: a pilot study.

Borderline Personality Disorder (BPD) symptoms include inappropriate control of anger and severe emotional dysregulation after rejection in daily life. Nevertheless, when using the Cyberball paradigm, a tossing game to simulate social exclusion, the seven basic emotions (happiness, sadness, anger, surprise, fear, disgust, and contempt) have not been exhaustively tracked out. It was hypothesized that these patients would show anger, contempt, and disgust during the condition of exclusion versus the condition of inclusion. When facial emotions are automatically detected by Artificial Intelligence, "blending", -or a mixture of at least two emotions- and "masking", -or showing happiness while expressing negative emotions- may be most easily traced expecting higher percentages during exclusion rather than inclusion. Therefore, face videos of fourteen patients diagnosed with BPD (26 ± 6 years old), recorded while playing the tossing game, were analyzed by the FaceReader software. The comparison of conditions highlighted an interaction for anger: it increased during inclusion and decreased during exclusion. During exclusion, the masking of surprise; i.e., displaying happiness while feeling surprised, was significantly more expressed. Furthermore, disgust and contempt were inversely correlated with greater difficulties in emotion regulation and symptomatology, respectively. Therefore, the automatic detection of emotional expressions during both conditions could be useful in rendering diagnostic guidelines in clinical scenarios.

Facial recognition, laterality judgement, alexithymia and resulting central nervous system adaptations in chronic primary headache and facial pain-A systematic review and meta-analysis.

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. 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. 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. 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. 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.

Acute neural effects of the mood stabiliser lamotrigine on emotional processing in healthy volunteers: a randomised control trial.

Lamotrigine is an effective mood stabiliser, largely used for the management and prevention of depression in bipolar disorder. The neuropsychological mechanisms by which lamotrigine acts to relieve symptoms as well as its neural effects on emotional processing remain unclear. The primary objective of this current study was to investigate the impact of an acute dose of lamotrigine on the neural response to a well-characterised fMRI task probing implicit emotional processing relevant to negative bias. 31 healthy participants were administered either a single dose of lamotrigine (300 mg, n = 14) or placebo (n = 17) in a randomized, double-blind design. Inside the 3 T MRI scanner, participants completed a covert emotional faces gender discrimination task. Brain activations showing significant group differences were identified using voxel-wise general linear model (GLM) nonparametric permutation testing, with threshold free cluster enhancement (TFCE) and a family wise error (FWE)-corrected cluster significance threshold of p < 0.05. Participants receiving lamotrigine were more accurate at identifying the gender of fearful (but not happy or angry) faces. A network of regions associated with emotional processing, including amygdala, insula, and the anterior cingulate cortex (ACC), was significantly less activated in the lamotrigine group compared to the placebo group across emotional facial expressions. A single dose of lamotrigine reduced activation in limbic areas in response to faces with both positive and negative expressions, suggesting a valence-independent effect. However, at a behavioural level lamotrigine appeared to reduce the distracting effect of fear on face discrimination. Such effects may be relevant to the mood stabilisation effects of lamotrigine.

Mist–fog-assisted real-time emotion recognition using deep transfer learning framework for smart city 4.0

Face emotion recognition (FER) has found several applications across numerous industries. One such application area lies in the field of human–computer interaction, where it can improve user experiences by allowing systems to adjust based on users’ emotional states by leveraging the capabilities of the Internet of Things (IoT) to facilitate more responsive interactions. The increasing demand for precise and immediate emotion analysis across various applications drives the need for FER utilizing deep transfer learning (DTL). In this study, a novel Mist-Fog framework for real-time FER is introduced, which employs a DTL algorithm called Mobile-Net for quick and easy image classification on mobile and embedded devices. It addresses the challenges of operating deep neural networks (DNN) under limits on power and processing resources. By utilizing depth-wise separable convolutions, the MobileNet algorithm reduces computational complexity while maintaining reasonable accuracy on the FER-2013 dataset. A Fog-based architecture, in which a mist layer comprising edge devices like Raspberry-Pi (RPi), an ARM-based single-board computer, was considered for a smart city scenario. Further, the M/M/1/K queuing system was considered, which follows the First-Come-First-Serve (FCFS) scheduling criteria and a Markov Modulated Poisson Process (MMPP) arrival rate to evaluate the system’s performance. The effectiveness of this novel approach towards industrial computer vision paradigms is assessed pertaining to different performance criteria.

Eye-tracking evidence of a relationship between attentional bias for emotional faces and depression severity in patients with treatment-resistant depression

In a retrospective study, 54 patients with treatment-resistant major depressive disorder (TRD) completed a free-viewing task in which they had to freely explore pairs of faces (an emotional face (happy or sad) opposite to a neutral face). Attentional bias to emotional faces was calculated for early and sustained attention. We observed a significant negative correlation between depression severity as measured by the 10-item Montgomery-Åsberg Depression Rating Scale (MADRS) and sustained attention to happy faces. In addition, we observed a positive correlation between depression severity and sustained attention to sad faces. No significant correlation between depression severity and early attention was found for either happy or sad faces. Although conclusions from the current study are limited by the lack of comparison with a control group, the eye-tracking free-viewing task appears to be a relevant, accessible and easy-to-use tool for measuring depression severity through emotional attentional biases in TRD.

Differentiating neural sensitivity and bias during face-emotion processing in youth: a computational approach.

The ability to interpret face-emotion displays is critical for the development of adaptive social interactions. Using a novel variant of a computational model and fMRI data, we examined behavioral and neural associations between two metrics of face-emotion labeling (sensitivity and bias) and age in youth. Youth and adults (n = 44, M age = 20.02, s.d. = 7.44, range = 8-36) completed an explicit face-emotion labeling fMRI task including happy to angry morphed face emotions. A drift-diffusion model was applied to choice and reaction time distributions to examine sensitivity and bias in interpreting face emotions. Model fit and reliability of parameters were assessed on adult data (n = 42). Linear and quadratic slopes modeled brain activity associated with dimensions of face-emotion valence and ambiguity during interpretation. Behaviorally, age was associated with sensitivity. The bilateral anterior insula exhibited a more pronounced neural response to ambiguity with older age. Associations between sensitivity and bias metrics and activation patterns indicated that systems encoding face-emotion valence and ambiguity both contribute to the ability to discriminate face emotions. The current study provides evidence for age-related improvement in perceptual sensitivity to facial affect across adolescence and young adulthood.

How do emotions affect giving? Examining the effects of textual and facial emotions in charitable crowdfunding

Drawing on emotional contagion theory and language-mediated association theory, this study develops a research model to examine how textual and facial emotions affect charitable crowdfunding performance. We use computer-aided techniques to extract and measure specific textual and facial emotions in pitches. The proposed model is tested via regression analysis with a sample of 1372 campaigns collected from the largest charitable crowdfunding platform in China—Tencent Gongyi. Moreover, we conducted a fuzzy-set qualitative comparative analysis to examine the complementarity of textual and facial emotions, which supplements the regression analysis results. Our findings show that both textual and facial emotions can impact funding outcomes. However, the effects of specific emotions vary: some (e.g., textual sadness and facial anger) are positive, some (e.g., textual anger and facial fear) are negative, and others (e.g., textual fear, textual disgust, and facial sadness) are insignificant. Moreover, facial emotions complement textual emotions in their effects on funding outcomes. This research outlines a framework to offer a more detailed and comprehensive understanding of emotions in charitable crowdfunding. It also contributes to existing research by revealing the vital but complex role of emotions in the persuasive process of prosocial behaviors and by uncovering the different cognitive mechanisms underlying the impacts of textual and facial emotions.

Smile, You’re on Camera: Investigating the Relationship between Selfie Smiles and Distress

Background: This study examined the relationship between (1) participant smiling in daily “selfie” videos and (2) self-reported distress. Given the extensive use of digital devices for sharing expressions of non-verbal behavior, and some speculation that these expressions may reveal psychological states—including emotional distress—we wanted to understand whether facial expression in these TikTok-like videos were correlated with standardized measures of psychological distress. Based on the work of Paul Ekman and others, which posits that facial expressions are universal reflections of people’s inner states, we predicted that smiling would be inversely related to psychological distress. Method: Twenty-four undergraduate students, aged 18+ years (M = 18.35, SD = 2.75), were prompted to record a two-minute selfie video each evening during two weeks of data collection (i.e., 14 total days). They were instructed to describe various aspects of their day. They also completed self-report questionnaires at the end of each assessment week, including the Depression Anxiety Stress Scale (DASS), Perceived Stress Scale (PSS), and the Pittsburgh Sleep Quality Index (PSQI). Results: A counterintuitive effect was observed whereby smiling intensity during selfie videos was positively correlated with individual differences in anxiety, depression, and stress. Discussion: This study challenges the common view that facial expressions necessarily reflect our inner emotions. It provides preliminary evidence that a mobile sensing app that captures selfies—along with other naturalistic data—may help elucidate the relationship between facial expressions and emotions.

Facial Emotion Recognition for Visually Impaired People using Transfer Learning

Individuals with visual impairment often face chal- lenges in social interactions, specifically at recognizing emotional cues. The proposed framework tackles this issue head-on by de- vising a Facial Emotion Recognition(FER) system, by employing an advanced Transfer Learning approach within Convolutional Neural Networks (CNNs). By leveraging the dataset FER-2013 [13], the proposed system aims to transcend the limitationsof traditional emotion recognition methods. Transfer learningallows the model to benefit from pre-trained knowledge on vast datasets, making it more efficient and effective in capturing complex facial features associated with different emotions. This approach is designed to offer better accuracy and generalization capabilities than other conventional methods. During training, the system will be designed to comprehensively capture the intricacies of facial expressions, enabling it to not only identify individuals but also interpret subtle changes in their emotional states throughout conversations. An innovative audio output system will be integrated into the FER system to provide a smoothand accessible experience for visually impaired users, allowing for a better understanding of social dynamics. By emphasizing transfer learning, this framework is designed to be efficient and robust, potentially revolutionizing emotional understanding for visually impaired individuals and setting a new standard in the field by showcasing the superior performance achievable throughadvanced machine learning techniques. Ultimately, this research aims to bridge the social gap for the visually impaired by fosteringinclusivity, independence, and safety in their daily life.

FACIAL IMAGE CAPTIONING USING DNN

Facial analysis, encompassing emotion, age, and gender detection, shows potential in various applications such as human-computer interaction, business, security, and health. This study delves into the development and evaluation of a deep neural network (DNN) model for facial emotion, age, and gender detection. Utilizing a convolutional neural network (CNN) architecture trained on diverse datasets for each task, our model proves effective in predicting facial features. The accuracy of needs assessment is X%, the marginal error (MAE) of age estimation is Y years, and the accuracy of gender classification is Z%.

Tango and physiotherapy interventions in Parkinson’s disease: a pilot study on efficacy outcomes on motor and cognitive skills

Pharmacological treatments in Parkinson’s disease (PD), albeit effective in alleviating many motor symptoms, have limited effects in non-motor signatures as cognitive impairment, as well as in other aspects included postural instability. Consequently, complementary interventions are nowadays a prerogative of clinical practice managing PD symptomatology. In this pilot longitudinal study, we recruited twenty-four PD patients participating in one of two interventions: adapted Argentine Tango or group-based physiotherapy. Participants underwent a motor and neuropsychological evaluation before and after four months of activities, carried out twice a week. We found a general stabilization of motor and cognitive abilities, with significant improvements in several motor skills, mainly pertaining to static and dynamic balance, similarly in both groups. At cognitive level, we measured a significant improvement in both groups in the Action Naming task. Interestingly, only PD patients in the Tango group improved their performance in the test measuring facial emotion recognition. These findings highlight the crucial role that physical activities have in the stabilization and slowdown of disease’s progression in PD. They further highlight the beneficial effects of a group-based physical intervention, which, especially in the case of Tango, could lead to behavioral ameliorations in domains other than the motor, such as emotion recognition.

Facial emotion recognition function and white matter microstructural alterations in drug-naive, comorbidity-free autism.

Individuals with autism spectrum disorder have deficits in facial emotion recognition and white matter microstructural alterations. Nonetheless, most previous studies were confounded by different variables, such as psychiatric comorbidities and psychotropic medications used by ASD participants. Also, it remains unclear how exactly FER deficits are related to white matter microstructural alterations in ASD. Accordingly, we aimed to investigate the FER functions, white matter microstructure, and their relationship in drug-naive and comorbidity-free ASD individuals. 59 ASD individuals and 59 typically developed individuals were included, where 46 ASD and 50 TD individuals completed FER tasks. Covariance analysis showed scores were lower in both basic and complex FER tasks in the ASD group. Tract-Based Spatial Statistics showed FA values in widespread white matter fibers were lower in the ASD group than in the TD group, including forceps major and forceps minor of the corpus callosum, anterior thalamic radiation, corticospinal tract, cingulum, inferior frontal-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus. Moreover, in the TD group but not the ASD group, the performance in the complex FER task was negatively correlated with the FA value in some white matter fibers, including forceps major of the corpus callosum, ATR, CT, cingulum, IFOF, ILF, SLF. Our study suggests children with ASD may experience deficits in facial emotion recognition and exhibit alterations in white matter microstructure. More importantly, our study indicates that white matter microstructural alterations may be involved in FER deficits in children with ASD.

FACIAL EMOTION RECOGNITION SYSTEM

Face Emotion recognition play a significance role in fields like aid, border management, police work, banking services, and client product. Facial expressions is wide utilized in social communication since they convey heaps of knowledge regarding folks, like moods, emotions, and alternative things. during this paper, we tend to review facial feeling recognition victimisation CNNs and highlight totally different algorithms and their performance impact. Further, we tend to demonstrate that utilizing CNNs during this field - ends up in a considerable performance increase. By forming associate ensemble of recent deep CNNs, we tend to get a FER2013 take a look at accuracy of 91.2%, outperforming previous works while not requiring auxiliary coaching knowledge or face registration. Key Words: Facial Expression, Confusion Matrix, Emotion Optimizer, Haarcascade classifier

Emotion expression salience and racially biased weapon identification: A diffusion modeling approach.

Racial stereotypes are commonly activated by informational cues that are detectable in people's faces. Here, we used a sequential priming task to examine whether and how the salience of emotion (angry/scowling vs. happy/smiling expressions) or apparent race (Black vs. White) information in male face primes shapes racially biased weapon identification (gun vs. tool) decisions. In two experiments (Ntotal = 546) using two different manipulations of facial information salience, racial bias in weapon identification was weaker when the salience of emotion expression versus race was heightened. Using diffusion decision modeling, we tested competing accounts of the cognitive mechanism by which the salience of facial information moderates this behavioral effect. Consistent support emerged for an initial bias account, whereby the decision process began closer to the "gun" response upon seeing faces of Black versus White men, and this racially biased shift in the starting position was weaker when emotion versus race information was salient. We discuss these results vis-à-vis prior empirical and theoretical work on how facial information salience moderates racial bias in decision-making.