Pictures Of Facial Expressions Research Articles

The power of personal control: Task choice attenuates the effect of implicit sadness on sympathetically mediated cardiac response.

Implicitly processed pictures of facial expressions of emotions have been found to systematically influence sympathetically mediated cardiovascular reactivity during task performance. According to the Implicit-Affect-Primes-Effort model, this happens because different affect primes activate the concepts of performance ease versus performance difficulty. Grounded in a recent action shielding model, our laboratory experiment (N = 129 university students) tested whether engaging in action by personal choice can immunize against those implicit affective influences on effort. Participants worked on an objectively difficult cognitive task, which was either externally assigned or ostensibly personally chosen. As predicted, participants in the assigned task condition showed weaker cardiac pre-ejection period reactivity during task performance, reflecting disengagement, when they were primed with sadness than when they were exposed to anger primes. Most relevant, this affect prime effect disappeared when participants could ostensibly choose their task themselves. These findings replicate previous research on implicit affect's impact on sympathetically mediated cardiac response and extend the literature on action shielding by personal choice effects to implicit affective influences on action execution.

Attention biases for emotional facial expressions during a free viewing task increase between 2.5 and 5 years of age.

The normative, developmental changes in affect-biased attention during the preschool years are largely unknown. To investigate the attention bias for emotional versus neutral faces, an eye-tracking measurement and free viewing of paired pictures of facial expressions (i.e., happy, fearful, sad, or angry faces) and nonface pictures with neutral faces were conducted with 367 children participating in a Finnish cohort study at the age of 2.5 years and with 477 children at the age of 5 years, 216 of which having follow-up measurements. We found an attention-orienting bias for happy and fearful faces versus neutral faces at both age points. An attention-orienting bias for sad faces emerged between 2.5 and 5 years. In addition, there were significant biases in sustained attention toward happy, fearful, sad, and angry faces versus neutral faces, with a bias in sustained attention for fearful faces being the strongest. All biases in sustained attention increased between 2.5 and 5 years of age. Moderate correlations in saccadic latencies were found between 2.5 and 5 years. In conclusion, attention biases for emotional facial expressions seem to be age-specific and specific for the attentional subcomponent. This implies that future studies on affect-biased attention during the preschool years should use small age ranges and cover multiple subcomponents of attention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Facial Expression Data Enhancement Method Induced by Improved StarGAN V2

Due to the small data and unbalanced sample distribution in the existing facial emotion datasets, the effect of facial expression recognition is not ideal. Traditional data augmentation methods include image angle modification, image shearing, and image scrambling. The above approaches cannot solve the problem that is the high similarity of the generated images. StarGAN V2 can generate different styles of images across multiple domains. Nevertheless, there are some defects in gener-ating these facial expression images, such as crooked mouths and fuzzy facial expression images. To service such problems, we improved StarGAN V2 by solving the drawbacks of creating pictures that apply an SENet to the generator of StarGAN V2. The generator’s SENet can concentrate at-tention on the important regions of the facial expression images. Thus, this makes the generated symmetrical expression image more obvious and easier to distinguish. Meanwhile, to further im-prove the quality of the generated pictures, we customized the hinge loss function to reconstruct the loss functions that increase the boundary of real and fake images. The created facial expression pictures testified that our improved model could solve the defects in the images created by the original StarGAN V2. The experiments were conducted on the CK+ and MMI datasets. The correct recognition rate of the facial expressions on the CK+ was 99.2031%, which is a 1.4186% higher accuracy than that of StarGAN V2. The correct recognition rate of the facial expressions on the MMI displays was 98.1378%, which is 5.059% higher than that of the StarGAN V2 method. Furthermore, contrast test outcomes proved that the improved StarGAN V2 performed better than most state-of-the-art methods.

A CNN-Based Facial Expression Recognition System

Facial expression recognition has long attracted researchers from a variety of domains, such as human-computer interaction, emotion analysis, intelligent medical care, and so on. Various human-designed features have been used in FER to extract image appearance features, but human-designed methods are difficult to be used to recognize new face images, which poses challenges for FER in uncontrolled environments. Recently, CNNs and FNNs have been used for facial expression recognition. The new CNN-based recognition approach yields excellent FER results. The numerous convolution and pooling layers that CNN possesses allow it to extract higher and multi-level features of the entire face or local regions. Additionally, CNN has excellent classification performance when it comes to the feature extraction of facial expression pictures. However, the mechanism through which FNN remains largely controversial. In this work, the experiments are carried out on the comparison of three facial expression recognition algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), and the experimental results show that, using CNN can get more stable recommendation results of better quality.

Enhanced Emotion Recognition in Videos: A Convolutional Neural Network Strategy for Human Facial Expression Detection and Classification

The human face is essential in conveying emotions, as facial expressions serve as effective, natural, and universal indicators of emotional states. Automated emotion recognition has garnered increasing interest due to its potential applications in various fields, such as human-computer interaction, machine learning, robotic control, and driver emotional state monitoring. With artificial intelligence and computational power advancements, visual emotion recognition has become a prominent research area. Despite extensive research employing machine learning algorithms like convolutional neural networks (CNN), challenges remain concerning input data processing, emotion classification scope, data size, optimal CNN configurations, and performance evaluation. To address these issues, we propose a comprehensive CNN-based model for real-time detection and classification of five primary emotions: anger, happiness, neutrality, sadness, and surprise. We employ the Amsterdam Dynamic Facial Expression Set – Bath Intensity Variations (ADFES-BIV) video dataset, extracting image frames from the video samples. Image processing techniques such as histogram equalization, color conversion, cropping, and resizing are applied to the frames before labeling. The Viola-Jones algorithm is then used for face detection on the processed grayscale images. We develop and train a CNN on the processed image data, implementing dropout, batch normalization, and L2 regularization to reduce overfitting. The ideal hyperparameters are determined through trial and error, and the model's performance is evaluated. The proposed model achieves a recognition accuracy of 99.38%, with the confusion matrix, recall, precision, F1 score, and processing time further quantifying its performance characteristics. The model's generalization performance is assessed using images from the Warsaw Set of Emotional Facial Expression Pictures (WSEFEP) and Extended Cohn-Kanade Database (CK+) datasets. The results demonstrate the efficiency and usability of our proposed approach, contributing valuable insights into real-time visual emotion recognition.

Attentional bias for sad facial expressions in adults with a history of peer victimization.

Previous research has indicated altered attentional processing in individuals with experiences of maltreatment or victimization in childhood and adolescence. The present study examined the impact of child and adolescent experiences of relational peer victimization on attentional processes in adulthood when confronted with emotional facial expressions. As part of an online study, a community sample of adults completed a facial dot-probe task. In the present task, pictures of facial expressions displaying four different emotions (anger, disgust, happiness, and sadness) were used. The results of the hierarchical regression analyses showed that retrospective reports of peer victimization made a significant contribution to the prediction of facilitated orienting processes for sad facial expressions. Experiences of emotional child maltreatment, on the other hand, made a significant contribution to the prediction of attentional biases for angry facial expressions. Our results emphasize the relevance of experiences of emotional and relational maltreatment in childhood and in adolescence for the processing of social stimuli in adulthood. The findings regarding emotional child maltreatment are more indicative of attentional biases in the context of threat detection, whereas the altered attentional processes in peer victimization are more indicative of mood-congruent biases. These altered processes may be active in social situations and may therefore influence future social situations, behavior, feelings, and thus mental health.

Study on the Features of Public Space for Children's Emotional Healing Based on Facial Emotion Recognition Technology

This paper uses facial emotion recognition and machine learning techniques to explore the influence of spatial features of children's activity areas on children's emotions. Through the children's evaluation experiment on the emotional quality of public spaces, we obtained the facial expression pictures and the questionnaire data of the emotional self-assessment scale (SAM) when children used the public space. Then use MEGVII facial emotion recognition API to recognize children's facial expressions, and form a facial emotion feature variable dataset. Then, a public space emotional quality model was established using decision tree (DT), neural network (NN), and random forest (RF) classifiers. Finally, the model's performance was evaluated through the confusion matrix, and five groups of data reserved in the original dataset were used for external validation of the model. The results show that: 1) In the binary classification model, the classification accuracy of the emotional valence of the NN classifier was 94.44%, and the classification accuracy of arousal was 84.62%. These two results outperform the models built by the DT and RF classifiers. In the three-class model, the classification accuracy of the emotional valence of the DF classifier is 71.88%, and the accuracy of arousal is 84.62%. These two results outperformed the models built by NN and RF classifiers. 2) The analysis results of the correlation between spatial features and emotional quality showed that the width of the bordering tree, width-to-height-ratio of the bordering tree (w2/h2), the number of layers of the border, the continuity of the spatial border, the proportion of facilities, the color of the space, the number of types and pavement color types were usually correlated with the spatial emotional valence.

Emotional response evoked by viewing facial expression pictures leads to higher temporal resolution.

We examined the effects of emotional response, with different levels of valence and arousal, on the temporal resolution of visual processing by using photos of various facial expressions. As an index of the temporal resolution of visual processing, we measured the minimum lengths of the noticeable durations for desaturated photographs using the method of constant stimuli by switching colorful facial expression photographs to desaturated versions of the same photographs. Experiments 1 and 2 used facial photographs that evoke various degrees of arousal and valence. Those photographs were prepared not only in an upright orientation but also in an inverted orientation to reduce emotional response without changing the photographs' image properties. Results showed that the minimum duration to notice monochrome photographs for anger, fear, and joy was shorter than that for a neutral face when viewing upright face photographs but not when viewing inverted face photographs. For Experiment 3, we used facial expression photographs to evoke various degrees of arousal. Results showed that the temporal resolution of visual processing increased with the degree of arousal. These results suggest that the arousal of emotional responses evoked by viewing facial expressions might increase the temporal resolution of visual processing.

Padova Emotional Dataset of Facial Expressions (PEDFE): A unique dataset of genuine and posed emotional facial expressions.

Facial expressions are among the most powerful signals for human beings to convey their emotional states. Indeed, emotional facial datasets represent the most effective and controlled method of examining humans' interpretation of and reaction to various emotions. However, scientific research on emotion mainly relied on static pictures of facial expressions posed (i.e., simulated) by actors, creating a significant bias in emotion literature. This dataset tries to fill this gap, providing a considerable amount (N = 1458) of dynamic genuine (N = 707) and posed (N = 751) clips of the six universal emotions from 56 participants. The dataset is available in two versions: original clips, including participants' body and background, and modified clips, where only the face of participants is visible. Notably, the original dataset has been validated by 122 human raters, while the modified dataset has been validated by 280 human raters. Hit rates for emotion and genuineness, as well as the mean, standard deviation of genuineness, and intensity perception, are provided for each clip to allow future users to select the most appropriate clips needed to answer their scientific questions.

Multimodal Emotion Recognition in Response to Oil Paintings.

Most previous affective studies use facial expression pictures, music or movie clips as emotional stimuli, which are either too simplified without contexts or too dynamic for emotion annotations. In this work, we evaluate the effectiveness of oil paintings as stimuli. We develop an emotion stimuli dataset with 114 oil paintings selected from subject ratings to evoke three emotional states (i.e., negative, neutral and positive), and acquire both EEG and eye tracking data from 20 subjects while watching the oil paintings. Furthermore, we propose a novel affective model for multimodal emotion recognition by 1) extracting informative features of EEG signals from both the time domain and the frequency domain, 2) exploring topological information embedded in EEG channels with graph neural networks (GNNs), and 3) combining EEG and eye tracking data with a deep autoencoder neural network. From the exper-iments, our model obtains an averaged classification accuracy of 94.72 % ± 1.47 %, which demonstrates the feasibility of using oil paintings as emotion elicitation material.

Reduced Child-Oriented Face Mirroring Brain Responses in Mothers With Opioid Use Disorder: An Exploratory Study.

While the prevalence of opioid use disorder (OUD) among pregnant women has multiplied in the United States in the last decade, buprenorphine treatment (BT) for peripartum women with OUD has been administered to reduce risks of repeated cycles of craving and withdrawal. However, the maternal behavior and bonding in mothers with OUD may be altered as the underlying maternal behavior neurocircuit (MBN) is opioid sensitive. In the regulation of rodent maternal behaviors such as licking and grooming, a series of opioid-sensitive brain regions are functionally connected, including the ventral pallidum (VP). In humans, these brain regions, interact with the supplementary motor area (SMA) to regulate maternal behaviors and are functionally dysregulated by opioids. It is unclear how these brain regions respond to the emotions of their child for mothers receiving BT. In this functional magnetic resonance imaging (fMRI) pilot study in 22 mothers within the first postpartum year, including six mothers receiving BT and 16 non-OUD mothers as a comparison group (CG), we devised a child face mirroring task in fMRI settings to assess maternal responses to pictures of facial expressions of own child and an unknown child in an empathic mirroring condition (Join) and a non-mirroring observation condition (Observe). In each condition, faces of neutral, ambiguous, distressed, and joyful expressions of each child were repeatedly displayed in a random order. The response of SMA during empathic mirroring (Join) vs. non-mirroring (Observe) of own child was reduced among BT/OUD vs. CG. Within MBN, the left VP, critical for parental sensitivity, had a similar deficit. This study outlines potential mechanisms for investigating the risks of deficits in the neural responses to actual maternal sensitivity and parenting behavior in mothers with OUD, and potential targets for interventions that reduce stress and augment maternal behavior and child outcome.

Touchscreen Technology to Improve Dementia Care Management for Older Adults Living in the Community

Chronic disease management challenges for persons living with dementia (PLWD) often includes difficulties communicating how their feeling or describing symptoms to caregivers. To address this issue, an interdisciplinary team is developing a touchscreen mobile technology that will allow the assessment of clinical information about the PLWD, such as sleep quality, appetite, pain level, and mood. The mobile technology also allows caregivers to share this information with providers in real-time from their homes. In the team’s effort to incorporate a graphic measure to assess mood into the technology, a literature review revealed no such measurement tool has been established and validated for assessment with PLWD. This study focused on developing a graphic clinical assessment tool that can be used to assess mood for PLWD and establishing the face and content validity for this assessment tool. A team that included multidisciplinary clinical experts and a graphic artist designed a series of pictures of facial expressions and body language depicting a continuum of mood states (i.e., happy through depressed). The research team consulted with existing artistic depictions of sadness expression, including medical illustrations depicting depression. Multiple iterations have resulted in a series of images that represent moods from happy to very sad. The team considered a number of factors in developing the images including gender, ethnicity, and race. Consensus on the construct validity was achieved by the expert clinical panel. Details of a follow-up study to evaluate construct validity will be presented.

Psychopathy and the perception of the genuineness of facial expressions

Individuals with psychopathic traits are thought to have deficits in emotion recognition, yet they are also known for being manipulative and conning, a skill that necessitates sensitivity to social cues. One unanswered question is whether psychopathic individuals can recognize subtleties in the expression of emotion, such as whether an expression is genuine or not. We examined relationships between psychopathy and ratings of the “genuineness” of pictures of facial expressions. Undergraduate participants (n = 453; 32.5% identifying as men) completed the Triarchic Psychopathy Measure and the Self-Report Psychopathy Scale III. They viewed images that depicted faces displaying either fake (forced, posed) or genuine expressions of happiness, sadness, surprise, and fear and rated how fake versus real they thought the expression was. Controlling for gender, psychopathic traits on both measures were associated with making less of a distinction between fake and real images. Individuals higher in psychopathy rated genuine expressions as less real than those lower in psychopathy. Analyses of psychopathy factors revealed a distinct finding for Boldness (interpersonal dominance and fearlessness), which was associated with rating real smiles as more real. Overall, the findings suggest that individuals higher in psychopathy may perceive the sincerity of emotions differently, which may influence their social behavior.

Early Right Motor Cortex Response to Happy and Fearful Facial Expressions: A TMS Motor-Evoked Potential Study.

The ability to rapidly process others’ emotional signals is crucial for adaptive social interactions. However, to date it is still unclear how observing emotional facial expressions affects the reactivity of the human motor cortex. To provide insights on this issue, we employed single-pulse transcranial magnetic stimulation (TMS) to investigate corticospinal motor excitability. Healthy participants observed happy, fearful and neutral pictures of facial expressions while receiving TMS over the left or right motor cortex at 150 and 300 ms after picture onset. In the early phase (150 ms), we observed an enhancement of corticospinal excitability for the observation of happy and fearful emotional faces compared to neutral expressions specifically in the right hemisphere. Interindividual differences in the disposition to experience aversive feelings (personal distress) in interpersonal emotional contexts predicted the early increase in corticospinal excitability for emotional faces. No differences in corticospinal excitability were observed at the later time (300 ms) or in the left M1. These findings support the notion that emotion perception primes the body for action and highlights the role of the right hemisphere in implementing a rapid and transient facilitatory response to emotional arousing stimuli, such as emotional facial expressions.

Facial emotion detection in Vestibular Schwannoma patients with and without facial paresis

ABSTRACT This study investigates whether there exist differences in facial emotion detection accuracy in patients suffering from Vestibular Schwannoma (VS) due to their facial paresis. Forty-four VS patients, half of them with, and half of them without a facial paresis, had to classify pictures of facial expressions as being emotional or non-emotional. The visual information of images was systematically manipulated by adding different levels of visual noise. The study had a mixed design with emotional expression (happy vs. angry) and visual noise level (10% to 80%) as repeated measures and facial paresis (present vs. absent) and degree of facial dysfunction as between subjects’ factors. Emotion detection accuracy declined when visual information declined, an effect that was stronger for anger than for happy expressions. Overall, emotion detection accuracy for happy and angry faces did not differ between VS patients with or without a facial paresis, although exploratory analyses suggest that the ability to recognize emotions in angry facial expressions was slightly more impaired in patients with facial paresis. The findings are discussed in the context of the effects of facial paresis on emotion detection, and the role of facial mimicry, in particular, as an important mechanism for facial emotion processing and understanding.

Effects of aging on emotion recognition from dynamic multimodal expressions and vocalizations

Age-related differences in emotion recognition have predominantly been investigated using static pictures of facial expressions, and positive emotions beyond happiness have rarely been included. The current study instead used dynamic facial and vocal stimuli, and included a wider than usual range of positive emotions. In Task 1, younger and older adults were tested for their abilities to recognize 12 emotions from brief video recordings presented in visual, auditory, and multimodal blocks. Task 2 assessed recognition of 18 emotions conveyed by non-linguistic vocalizations (e.g., laughter, sobs, and sighs). Results from both tasks showed that younger adults had significantly higher overall recognition rates than older adults. In Task 1, significant group differences (younger > older) were only observed for the auditory block (across all emotions), and for expressions of anger, irritation, and relief (across all presentation blocks). In Task 2, significant group differences were observed for 6 out of 9 positive, and 8 out of 9 negative emotions. Overall, results indicate that recognition of both positive and negative emotions show age-related differences. This suggests that the age-related positivity effect in emotion recognition may become less evident when dynamic emotional stimuli are used and happiness is not the only positive emotion under study.

Structured Learning and Prediction in Facial Emotion Classification and Recognition

Structured prediction methods have become, in recent years, an attractive tool for many machine-learning applications especially in the image processing area as in customers satisfaction prediction by using facial recognition systems, in criminal investigations based on face sketches recognition, in aid to autistic children and so. The main objective of this paper is the identification of the emotion of the human being, based on their facial expressions, by applying structured learning and perfect face ratios. The basic idea of our approach is to extract the perfect face ratios from a facial emotion image as the features, this face emotional images are labeled with their kind of emotions (the seven emotions defined in literature). For this end, first we determined sixty-eight landmarks point of image faces, next we applied a new deep geometric descriptor to calculate sixteen features representing the emotional face. The training and the testing tasks are applied to the Warsaw dataset: The Set of Emotional Facial Expression Pictures (WSEFEP) dataset. Our proposed approach can be also applied in others competitor facial emotion datasets. Based on experiments, the evaluation demonstrates the satisfactory performance of our applied method, the recognition rate reaches more than 97% for all seven emotions studied and it exceeds 99.20% for neutral facial images.

The McGill Face Database: Validation and Insights Into the Recognition of Facial Expressions of Complex Mental States.

Current databases of facial expressions represent only a small subset of expressions, usually the basic emotions (fear, disgust, surprise, happiness, sadness, and anger). To overcome these limitations, we introduce a database of pictures of facial expressions reflecting the richness of mental states. A total of 93 expressions of mental states were interpreted by two professional actors, and high-quality pictures were taken under controlled conditions in front and side view. The database was validated in two experiments. First, a four-alternative forced-choice paradigm was employed to test the ability to select a term associated with each expression. Second, the task was to locate each face within a 2-D space of valence and arousal. Results from both experiments demonstrate that subjects can reliably recognize a great diversity of emotional states from facial expressions. While subjects’ performance was better for front view images, the advantage over the side view was not dramatic. This is the first demonstration of the high degree of accuracy human viewers exhibit when identifying complex mental states from only partially visible facial features. The McGill Face Database provides a wide range of facial expressions that can be linked to mental state terms and can be accurately characterized in terms of arousal and valence.

Children's Perceptions of Pictures Intended to Measure Anxiety During Hospitalization

Many children experience anxiety during a hospital stay, which can have an adverse impact on their recovery and response to future hospital care. To facilitate assessment of anxiety in young children and children with communicative disabilities, the short S-STAI (short version of the State scale of the State-Trait Anxiety Inventory), has previously been adapted to a visual format. The aims of the present study were (a) to validate the pictures used to represent emotions and the steps of a quantitative scale (‘a lot’ – ‘some’ – ‘a little’), including to determine whether any of them should be replaced, and (b) to assess different pictures to be used for a qualitative scale (‘good/like’ – ‘in between/so-so’ –‘bad/dislike’) in the same instrument. A total of 103 children aged 5–8 years were asked to choose verbal labels for pictures of facial expressions intended to represent emotions, match those pictures with the emotional categories used in the short S-STAI (Tense/Nervous, Worried/Afraid, Calm/Relaxed, and Happy/Content) and indicate their preferences for pictures intended to represent the steps of a quantitative scale and a qualitative one. The children understood both scales and the differences between positive and negative emotions in the short S-STAI. The older children (aged 7 and 8) significantly more often chose a picture for each step of the scales that was intended to represent that particular step. The article discusses implications for the choice of pictures representing emotional states in the short S-STAI and presents recommended pictures.

Combining Facial Expression and Touch for Perceiving Emotional Valence

We conducted an experimental study to investigate how the participants combine touch and facial expression to evaluate emotional valence. In this study, visual and haptic stimuli were presented separately and then presented together. The visual stimuli comprised pictures of facial expressions with different emotional levels, and the touch stimuli consisted of air jet tactile stimulation performed on the arms of the participants. The participants were asked to evaluate the communicated emotional valence on a continuous scale. Information Integration Theory was used to model the algebraic rule (addition, multiplication, averaging, and so on) that underlies the multimodal perception of emotional valence. Analyses showed that the participants usually integrated both visual and touch information to evaluate the emotional valence. The main integration rule was averaging, with nonsystematic predominance of each modality over the other modality.