Recognition Of Emotional Expressions Research Articles

O reconhecimento de expressões faciais de emoção e os problemas interpessoais diferem entre alcoolistas em diferentes estágios de abstinência?

O reconhecimento de expressões faciais de emoção e os problemas interpessoais diferem entre alcoolistas em diferentes estágios de abstinência?

Emotional face expressions recognition in childhood: developmental markers, age and sex effect.

Recognizing emotional face expressions in others is a valuable non-verbal communication and particularly relevant throughout childhood given that children's language skills are not yet fully developed, but the first interactions with peers have just started. This study aims to investigate developmental markers of emotional facial expression in children and the effect of age and sex on it. A total of 90 children split into three age groups: 6-7years old (n = 30); 8-9years old (n = 30); 10-11years old (n = 30) took part in the study. Participants were exposed to 38 photos in two exposure times (500ms and 1000ms) of children expressing happiness, sadness, anger, disgust, fear and surprise on three intensities, plus images of neutral faces. Happiness was the easiest expression to be recognized, followed by disgust and surprise. As expected, 10-11-year-old group showed the highest accuracy means, whereas 6-7-year-old group had the lowest means of accuracy. Data support the non-existence of female advantage.

Non‐invasive neuromodulation can reduce aggressive behaviors in humans: A critical perspective

Containing aggressive behavior is an ongoing challenge in modern society. Aggressiveness is a multi-level construct that can be driven by emotions (reactive aggression) or can be "cold-blooded" and goal-directed (proactive). Aggressive behavior could arise because of a misjudgment of others' intentions or can follow frontal brain lesions leading to a reduction of impulse control and emotion regulation. In the last few years, interventional and basic research studies adopting Non-Invasive Brain Stimulation (NIBS) have significantly risen. Those techniques have been used both in healthy people, to better understand the role of certain brain regions in psychological processes, and in aggressive subjects to improve their symptoms. From an overview of the literature, focusing on the paper that uses transcranial direct current stimulation (tDCS) to reduce aggressiveness, it emerges that tDCS can (i) enhance facial emotion expression recognition, (ii) improve impulses control, and (iii) affect approach/withdrawal motivation. The current work shows the strengths and weaknesses of tDCS intervention on aggressive individuals, suggesting that this instrument could be adopted on violent people, and paves the way for intervention in some applied settings such as prison.

Recognition of facial emotion expressions and perceptual processes in 22q11.2 deletion syndrome.

Social cognition (SC) deficits and of its facial emotion expression (FEE) component have been described in 22q11.2 Deletion Syndrome (22q11.2DS), a high-risk for schizophrenia (SCZ) systemic genetic syndrome. Correlations between deficits in FEE skills and visual-spatial abilities in people with 22q11.2DS warrant investigation. The sample consisted of 37 patients with 22q11.2DS (DEL), 19 with 22q11.2DS and psychosis (DEL-SCZ), 23 with idiopathic SCZ, and 48 healthy controls. We assessed FEE through The Ekman 60 Faces test (EK-F60), visual-spatial skills with Raven's Standard Progressive Matrices, and symptom severity with the positive And negative syndrome scale. Statistics were conducted through multivariate analysis of variance and correlation analysis. Patients with 22q11.2DS performed worse that the other groups in recognizing Surprise, Disgust, Rage, Fear, and Neutral expressions on the EK-F60. Recognition of Surprise and Disgust correlated positively with visual-spatial abilities in patients with 22q11.2DS; negative and cognitive symptoms correlated negatively with recognition of Sadness, Surprise, and Disgust. Patients with 22q11.2DS show impairments of both peripheral and central steps of the emotional recognition process, leading to SC deficits. The latter are present regardless of the presence of a full-blown psychosis.

The role of sex and emotion on emotion perception in artificial faces: An ERP study

Sex has a significant impact on the perception of emotional expressions. However, it remains unclear whether sex influences the perception of emotions in artificial faces, which are becoming popular in emotion research. We used an emotion recognition task with FaceGen faces portraying six basic emotions aiming to investigate the effect of sex and emotion on behavioural and electrophysiological parameters. 71 participants performed the task while EEG was recorded. The recognition of sadness was the poorest, however, females recognized sadness better than males. ERP results indicated that fear, disgust, and anger evoked higher amplitudes of late positive potential over the left parietal region compared to neutral expression. Females demonstrated higher values of global field power as compared to males. The interaction between sex and emotion on ERPs was not significant. The results of our study may be valuable for future therapies and research, as it emphasizes possibly distinct processing of emotions and potential sex differences in the recognition of emotional expressions in FaceGen faces.

Recognition of emotional face expressions in patients with restless legs syndrome

Objective Restless legs syndrome (RLS) is one of the commonest neurologic diseases. Along with sensory and motor symptoms, cognitive impairment and psychiatric features can be seen with RLS. The present study, was planned to look for evidence of cognitive impairment by evaluating facial emotion recognition (FER) in patients with RLS. Methods In this study, 80 patients with RLS and 50 healthy controls (HCs) were included. Demographic data were recorded. All patients with RLS and HCs were tested with Beck anxiety inventory (BAI), Beck depression inventory (BDI) and with Ekman’s test for recognition of facial emotions. Results Sixty-three of the patients with RLS and 37 of the HCs were female. The mean age of the patients was 45.41 ± 8.24, and the mean age of HCs was 43.12 ± 10.35. The patients and HCs were similar regarding sex, age, educational status, and marital status. Patients with RLS had FER difficulties comparing HCs. There was a negative correlation between Ekman’s test scores and BDI (r = −0.311, p < 0.001) and BAI scores (r = −0.379, p < 0.001). Conclusion FER is an invaluable research topic regarding cognitive function in RLS, which may help us develop different perspectives in terms of revealing the pathophysiology and is very important for the well-being of the patients’ social interactions.

The association between language and recognition of facial emotional expressions in elderly individuals.

To check the association between a good performance of language and the recognition of facial emotional expressions in elderly individuals. Transversal study performed with 118 elderly individuals from the primary care services of health of a city in the state of São Paulo. Sociodemographic data were collected, regarding the performance of language through the domain of Addenbrooke Cognitive Examination - Revised and Recognition of Facial Emotional Expressions. The sample was divided in thirds according to the performance of language: T1 = the best, T2 = average, and T3 = the worst. The groups T1xT3 were compared regarding the performance of recognition of facial expressions of anger, disgust, fear, happiness, sadness, and surprise, and for the intensities of 40%, 60%, 80%, and 100%. The association of independent variables over the performance of language was analyzed through logistic regression. The multivariate model was built from the results of the univariate analyses and has included the continuous variables by emotion and by intensity. Age and schooling associated to the performance of language in the univariate model were included in the multivariate model in order to adjust association analyses. The sample was mainly female (84.7%), with an average age of 70.5 years old, and 3.5 schooling years. The variables associated to the best performance of language in comparative analysis of T1 and T3 were: surprise (OR = 1.485, IC 95% 1.194 - 1.846), and disgust (OR = 1.143, IC 95% 1.005 - 1.300). The recognition of facial emotional expressions of surprise and disgust were shown as important factors associated to the good performance of language.

Associação entre linguagem e o reconhecimento de expressões faciais de emoções em idosos

ABSTRACT Purpose To check the association between a good performance of language and the recognition of facial emotional expressions in elderly individuals. Methods Transversal study performed with 118 elderly individuals from the primary care services of health of a city in the state of São Paulo. Sociodemographic data were collected, regarding the performance of language through the domain of Addenbrooke Cognitive Examination – Revised and Recognition of Facial Emotional Expressions. The sample was divided in thirds according to the performance of language: T1 = the best, T2 = average, and T3 = the worst. The groups T1xT3 were compared regarding the performance of recognition of facial expressions of anger, disgust, fear, happiness, sadness, and surprise, and for the intensities of 40%, 60%, 80%, and 100%. The association of independent variables over the performance of language was analyzed through logistic regression. The multivariate model was built from the results of the univariate analyses and has included the continuous variables by emotion and by intensity. Age and schooling associated to the performance of language in the univariate model were included in the multivariate model in order to adjust association analyses. Results The sample was mainly female (84.7%), with an average age of 70.5 years old, and 3.5 schooling years. The variables associated to the best performance of language in comparative analysis of T1 and T3 were: surprise (OR = 1.485, IC 95% 1.194 – 1.846), and disgust (OR = 1.143, IC 95% 1.005 – 1.300). Conclusion The recognition of facial emotional expressions of surprise and disgust were shown as important factors associated to the good performance of language.

Impaired Recognition of Emotional Facial Expressions in Adults with Attention Deficit/Hyperactivity Disorder: A Review of Event Related Potential (ERP) Studies

Background: The link between attention deficit/hyperactivity disorder (ADHD) and impaired recognition of emotional facial expressions is well documented. The cause for this impairment is, however, unclear. Some studies suggest that it may be due to deficits in emotional processing, while others attribute it to inattention. Purpose: This review examines the temporal evolution of the response of adults with ADHD to emotional facial expressions. The process is assessed using Event Related Potential (ERP), a technique that follows the response with millisecond resolution. Method: An integrated, systematic search of relevant databases based on the Whittemore and Knafl and the PRISMA 2020 review methodologies was applied. Ten studies met all the inclusion criteria. Results: Behavioral data (such as response time) confirm that adults with ADHD have some deficits in their ability to identify emotional facial expressions when compared to healthy controls. However, their degree of impairment varies with expression type. Analysis of the time-evolution of the response, as measured by ERP, shows that the response of adults with ADHD is heightened, when compared to healthy peers, in both initial and later stages. In the intermediate window, at approximately 300 ms, adults with ADHD show muted EPR amplitudes when compared to their healthy peers. The same time-evolution of response was observed for both emotional expressions and neutral ones. Conclusions: Overall, adults with ADHD display some level of impairment in their ability to recognize emotional facial expressions that is dependent on the expression valence. The time-evolution of the responses suggests the impairment might be linked to a lapse in attention at roughly 300 ms post-stimulus.

Voice, Body Cues and Facial Expression in Emotion Recognition of Spanish Children and Adolescents: The Validation of Bell-Lysaker Emotion Recognition Test.

Emotion recognition has been traditionally measured trough the recognition of emotional expressions of static faces. Studies suggest that emotion recognition is progressively acquired from early stages in our infancy. However, the literature regarding other emotional domains such as voice or body movements is scarce. Additionally, the number of tools that integrate several domains is limited, especially in children and adolescents, and none of them tested in Spanish samples. Therefore, this study aimed to define the psychometric properties of the Bell-Lysaker Emotion Recognition Task (BLERT) and a new-designed alternate version providing normative data in Spanish children and adolescents (from 8 to 15 years old corresponding to 3th). Moreover, we aim to describe the emotional acquisition trajectory of children and adolescents with a tool that integrates voice, face expressions and body movements. For that purpose, BLERT was translated into Spanish (BLERT-SI) and an alternate version was created (BLERT-SII). A total of 545 children and adolescents from 8 to 15 year-old participated in the study (250 male/295 female). All participants fulfilled BLERT-SI and BLERT-SII within two weeks of difference. Order of presentation was counterbalanced. Results showed that BLERT-SI and SII have good internal consistency (α = .70 and 71 respectively). Test-retest reliability showed a moderate correlation (r = .45; p < .001). Percentages equivalences per age are provided. Age correlated with BLERT-SI (r = .31; p < .001) and BLERT-SII (r = .21; p < .001), showing a progressive acquisition and development of emotion recognition during this period. BLERT-SI and SII are useful tools when studying the follow-up of children and adolescents.

EEG Mapping of Cortical Activation Related to Emotional Stroop with Facial Expressions: A TREFACE Study

TREFACE (Test for Recognition of Facial Expressions with Emotional Conflict) is a computerized model for investigating the emotional factor in executive functions based on the Stroop paradigm, for the recognition of emotional expressions in human faces. To investigate the influence of the emotional component at the cortical level, the electroencephalographic (EEG) recording technique was used to measure the involvement of cortical areas during the execution of certain tasks. Thirty Brazilian native Portuguese-speaking graduate students were evaluated on their anxiety and depression levels and on their well-being at the time of the session. The EEG recording was performed in 19 channels during the execution of the TREFACE test in the 3 stages established by the model-guided training, reading, and recognition—both with congruent conditions, when the image corresponds to the word shown, and incongruent condition, when there is no correspondence. The results showed better performance in the reading stage and in congruent conditions, while greater intensity of cortical activation in the recognition stage and in incongruent conditions. In a complementary way, specific frontal activations were observed: intense theta frequency activation in the left extension representing the frontal recruitment of posterior regions in information processing; also, activation in alpha frequency in the right frontotemporal line, illustrating the executive processing in the control of attention, in addition to the dorsal manifestation of the prefrontal side, for emotional performance. Activations in beta and gamma frequencies were displayed in a more intensely distributed way in the recognition stage. The results of this mapping of cortical activity in our study can help to understand how words and images of faces can be regulated in everyday life and in clinical contexts, suggesting an integrated model that includes the neural bases of the regulation strategy.

Feelings first? Sex differences in affective and cognitive processes in emotion recognition.

The recognition of emotional expressions is important for social understanding and interaction, but findings on the relationship between emotion recognition, empathy, and theory of mind, as well as sex differences in these relationships, have been inconsistent. This may reflect the relative involvement of affective and cognitive processes at different stages of emotion recognition and in different experimental paradigms. In this study, images of faces were morphed from neutral to full expression of five basic emotions (anger, disgust, fear, happiness, and sadness), which participants were asked to identify as quickly and accurately as possible. Accuracy and response times from healthy males (n = 46) and females (n = 43) were analysed in relation to self-reported empathy (Empathy Quotient; EQ) and mentalising/theory of mind (Reading the Mind in the Eyes Test). Females were faster and more accurate than males in recognising dynamic emotions. Linear mixed-effects modelling showed that response times were inversely related to the emotional empathy subscale of the EQ, but this was accounted for by a female advantage on both measures. Accuracy was unrelated to EQ scores but was predicted independently by sex and Eyes Test scores. These findings suggest that rapid processing of dynamic emotional expressions is strongly influenced by sex, which may reflect the greater involvement of affective processes at earlier stages of emotion recognition.

Sensors and Artificial Intelligence Methods and Algorithms for Human-Computer Intelligent Interaction: A Systematic Mapping Study.

To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.

Sensitivity to Emotion Intensity and Recognition of Emotion Expression in Neurotypical Children.

This study assessed two components of face emotion processing: emotion recognition and sensitivity to intensity of emotion expressions and their relation in children age 4 to 12 (N = 216). Results indicated a slower development in the accurate decoding of low intensity expressions compared to high intensity. Between age 4 and 12, children discriminated high intensity expressions better than low ones. The intensity of expression had a stronger impact on overall face expression recognition. High intensity happiness was better recognized than low intensity up to age 11, while children 4 to 12 had difficulties discriminating between high and low intensity sadness. Our results suggest that sensitivity to low intensity expressions acts as a complementary mediator between age and emotion expression recognition, while this was not the case for the recognition of high intensity expressions. These results could help in the development of specific interventions for populations presenting socio-cognitive and emotion difficulties.

Impairment of EEG functional connectivity in dementia during recognition of emotional facial expression

AbstractBackgroundThe performance of facial expression recognition is affected both in neurocognitive disorders and in healthy elderly. Emotional content processing is expected to be adversely affected in dementia and as well in Parkinson's disease patients. The present study aims to compare the impaired facial expression recognition between different types of dementia patients, functional connectivity changes in patients with mild cognitive impairment, and patients with dementia were investigated by event‐related EEG‐coherence.Method25 Healthy elderly controls (HC), 25 Mild Cognitive Impairment patients (MCI), 25 patients with Alzheimer's disease (AD), 15 patients with Parkinson's disease with Mild Cognitive Impairment (PDMCI), and 16 patients with Parkinson's disease Dementia (PDD) were included in the study. EEG was recorded while angry, happy, and neutral facial expressions were presented. The artifacts were rejected by using Independent‐Component‐Analysis and Current‐Source‐Density. Magnitude‐squared coherence values of theta (4‐7Hz, 50‐400ms) responses were calculated. Inter‐hemispheric and intra‐hemispheric (frontal and central centered) pairs of F3, F4, C3, C4, T7, T8, Tp7, Tp8, P3, P4, O1, O2 channels were used. Two separate inter and intra hemispheric repeated‐measures‐ANOVA models were used for group comparisons.ResultThere were no group differences for intra‐hemispheric comparisons, but GroupXLocation comparison (p=.040) was significant, HE and MCI groups had higher coherence values in central‐ temporal, central‐ temporoparietal, and central‐ occipital connections compared to PDMCI, PDD, and AD patient groups (Figure 1). FaceXGroup (p=.009) interaction was significant for inter‐hemispheric coherence values, HC and MCI groups had higher interhemispheric coherence values than the PDMCI, PDD, and AD, especially during recognition of "angry" facial expression.ConclusionThe connectivity between the central and posterior regions increased with emotional face content; this increase seems to be preserved in MCI. On the other hand, it wasn't maintained in patients with more severe neurocognitive impairment; PDMCI, PDD, and AD groups had reduced central‐posterior connectivity and interhemispheric connectivity during the facial expression recognition paradigm. The decrease in inter and intra‐connectivity in the posterior regions is consistent with behavioral scores and may indicate neural losses as a phenomenon.

Convolutional Neural Network With Batch Normalization for Classification of Emotional Expressions Based on Facial Images

Emotion recognition through facial images is one of the most challenging topics in human psychological interactions with machines. Along with advances in robotics, computer graphics, and computer vision, research on facial expression recognition is an important part of intelligent systems technology for interactive human interfaces where each person may have different emotional expressions, making it difficult to classify facial expressions and requires training data. large, so the deep learning approach is an alternative solution., The purpose of this study is to propose a different Convolutional Neural Network (CNN) model architecture with batch normalization consisting of three layers of multiple convolution layers with a simpler architectural model for the recognition of emotional expressions based on human facial images in the FER2013 dataset from Kaggle. The experimental results show that the training accuracy level reaches 98%, but there is still overfitting where the validation accuracy level is still 62%. The proposed model has better performance than the model without using batch normalization.

Conduct problems among children in low-income, urban neighborhoods: A developmental psychopathology- and RDoC-informed approach

AbstractConduct problems are associated with numerous negative long-term psychosocial sequelae and are among the most frequent referrals for children's mental health services. Youth residing in low-income, urban communities are at increased risk for conduct problems, but not all youth in these environments develop conduct problems, suggesting heterogeneity in risk and resilience processes and developmental pathways. The present study used a developmental psychopathology- and Research Domain Criteria (RDoC)-informed approach for conceptualizing risk and resilience for conduct problems among children from low-income, urban neighborhoods. Participants were 104 children (M = 9.93 ± 1.22 years; 50% male; 96% African American, 4% Latinx). We assessed four constructs reflecting cognitive and neurobiological processes associated with conduct problems using multiple levels of analysis and informants: autonomic nervous system reactivity, limbic system/orbitofrontal cortical functioning, dorsolateral prefrontal cortical functioning, and conduct problems. Latent profile analysis identified four profiles: typically developing (TD, n = 34); teacher-reported conduct problems (TCP, n = 14); emotion processing (EP, n = 27); and emotion expression recognition (EER, n = 29). External validation analyses demonstrated that profiles differed on various indices of conduct problems in expected ways. The EP profile exhibited lower levels of emotional lability and callous–unemotional behaviors, and higher levels of prosocial behavior. The TD profile demonstrated elevated emotional lability. Implications for etiological and intervention models are presented.

A survey on facial emotion recognition techniques: A state-of-the-art literature review

In this survey, a systematic literature review of the state-of-the-art on emotion expression recognition from facial images is presented. The paper has as main objective arise the most commonly used strategies employed to interpret and recognize facial emotion expressions, published over the past few years. For this purpose, a total of 51 papers were analyzed over the literature totaling 94 distinct methods, collected from well-established scientific databases (ACM Digital Library, IEEE Xplore, Science Direct and Scopus), whose works were categorized according to its main construction concept. From the analyzed works, it was possible to categorize them into two main trends: classical and those approaches specifically designed by the use of neural networks. The obtained statistical analysis demonstrated a marginally better recognition precision for the classical approaches when faced to neural networks counterpart, but with a reduced capacity of generalization. Additionally, the present study verified the most popular datasets for facial expression and emotion recognition showing the pros and cons each and, thereby, demonstrating a real demand for reliable data-sources regarding artificial and natural experimental environments.

Recognition of vocal socioemotional expressions at varying levels of emotional intensity.

Nonverbal expressions of emotion can vary in intensity, from ambiguous to prototypical exemplars: for instance, facial displays of happiness may range from a faint smile to a full-blown grin. Previous work suggests that the accuracy with which facial expressions are recognized as the intended emotion increases with emotional intensity, although this pattern depends on the displayed emotion. Less is known about the association between emotional intensity and the recognition of vocal emotional expressions (affective prosody), which also convey information about others' socioemotional intent but are perceived and interpreted differently than facial expressions. The current study examined listeners' ability to recognize emotional intent in morphed vocal prosody recordings that varied in emotional intensity from neutral to prototypical exemplars of basic emotions (anger, disgust, fear, happiness, sadness) and social expressions (friendliness, meanness). Results suggest that listeners' accuracy in identifying the intended emotional intent in each recording increased nonlinearly with emotional intensity. This pattern varied by emotion type: for instance, accuracy for anger rose steeply with increasing emotional intensity before plateauing, whereas accuracy for happiness remained unchanged across low-intensity exemplars but increased thereafter. These findings highlight emotion-specific ways in which dynamic changes in emotional intensity inform perceptions of socioemotional intent in emotional prosody. Moreover, these results also point to potential challenges in emotional communication in social interactions that rely primarily on the voice, with many low-intensity expressions having a higher probability of being misinterpreted. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Facial Expression Emotion Recognition Model Integrating Philosophy and Machine Learning Theory.

Facial expression emotion recognition is an intuitive reflection of a person’s mental state, which contains rich emotional information, and is one of the most important forms of interpersonal communication. It can be used in various fields, including psychology. As a celebrity in ancient China, Zeng Guofan’s wisdom involves facial emotion recognition techniques. His book Bing Jian summarizes eight methods on how to identify people, especially how to choose the right one, which means “look at the eyes and nose for evil and righteousness, the lips for truth and falsehood; the temperament for success and fame, the spirit for wealth and fortune; the fingers and claws for ideas, the hamstrings for setback; if you want to know his consecution, you can focus on what he has said.” It is said that a person’s personality, mind, goodness, and badness can be showed by his face. However, due to the complexity and variability of human facial expression emotion features, traditional facial expression emotion recognition technology has the disadvantages of insufficient feature extraction and susceptibility to external environmental influences. Therefore, this article proposes a novel feature fusion dual-channel expression recognition algorithm based on machine learning theory and philosophical thinking. Specifically, the feature extracted using convolutional neural network (CNN) ignores the problem of subtle changes in facial expressions. The first path of the proposed algorithm takes the Gabor feature of the ROI area as input. In order to make full use of the detailed features of the active facial expression emotion area, first segment the active facial expression emotion area from the original face image, and use the Gabor transform to extract the emotion features of the area. Focus on the detailed description of the local area. The second path proposes an efficient channel attention network based on depth separable convolution to improve linear bottleneck structure, reduce network complexity, and prevent overfitting by designing an efficient attention module that combines the depth of the feature map with spatial information. It focuses more on extracting important features, improves emotion recognition accuracy, and outperforms the competition on the FER2013 dataset.