Facial Emotion Recognition Research Articles

Eye movement characteristics of emotional face recognizing task in patients with mild to moderate depression

ObjectiveDepression is a complex affective disorder characterized by high prevalence and severe impact, commonly presenting with cognitive impairment. The objective diagnosis of depression lacks precise standards. This study investigates eye movement characteristics during emotional face recognition task (EFRT) in depressive patients to provide empirical support for objective diagnosis.MethodsWe recruited 43 patients with depression (Depressive patients, DP) from a psychiatric hospital and 44 healthy participants (Healthy Control, HC) online. All participants completed an EFRT comprising 120 trials. Each trial presented a gray screen for 800 ms followed by a stimulus image for judgment. Emotions were categorized as positive, neutral, or negative. Eye movement trajectories were recorded throughout the task. Latency of First Fixation (LFF), Latency of First Fixation for Eye AOI, and Latency of First Fixation for Mouth AOI were used as representative indicators of early attention, Proportion of Eye AOI, and Proportion of Mouth AOI as measures of intermediate attention, Accuracy (ACC) and Reaction Time (RT) as behavioral indicators of late-stage attention. In this study, these metrics were employed to explore the differences between patients with depression and healthy individuals.ResultsCompared to healthy participants, individuals with depression exhibit longer first fixation latencies on the eyes and mouth during the early attention stage of emotional face recognition, indicating an avoidance tendency toward key facial recognition cues. In the mid-to-late attention stages, depressive individuals show an increased fixation ratio on the eyes and a decreased fixation ratio on the mouth, along with lower accuracy and longer response times. These findings suggest that, relative to healthy individuals, individuals with depression have deficits in facial recognition.ConclusionThis study identified distinct attention patterns and cognitive deficits in emotional face recognition among individuals with depression compared to healthy individuals, providing an attention-based approach for exploring potential clinical diagnostic markers for depression.

Age differences in mental state inference of sarcasm: Contributions of facial emotion recognition and cognitive performance.

Older age is associated with poorer ability to accurately infer mental states, but some mental states are more complex than others. Sarcasm is a complex mental state because the literal and intended meaning of a speaker's words are in opposition. Individuals must rely on additional cues (e.g., facial expressions, intonation) for accurate inference. We hypothesized that understanding of sarcastic versus sincere exchanges would be more sensitive to age-related difficulty in mental state understanding. We examined accuracy at identifying sarcasm among 263 adults (ages 18-90 years) using videos of social interactions in The Awareness of Social Inference Test. Hypotheses were tested using a logistic linear mixed effects model predicting correct/incorrect trial-level responses. To characterize why sarcasm differed with age, we measured two abilities commonly implicated in mental state understanding: facial emotion recognition and cognitive performance. Sarcasm understanding declined with age, whereas understanding of sincere exchanges did not. Both better emotion recognition and cognitive performance related to better understanding of sarcastic but not sincere exchanges. Only cognitive performance showed an age-related effect such that the cognitive performance among the oldest participants facilitated their understanding of both sarcastic and sincere exchanges. We showed that individual variation related to age and social and cognitive performance is more pronounced when the use of multiple mental state cues is more (sarcasm) or less (sincerity) necessary for accurate understanding of social interactions. Naturalistic paradigms involving multiple mental state cues can address important questions about how older adults make decisions in the real world.

Facial Emotion Recognition Using Residual Neural Networks

Facial Emotion Recognition Using Residual Neural Networks

Behavioral and neural evidence for difficulty recognizing masked emotional faces.

Facial emotion recognition is vital for human social behavior. During the COVID-19 pandemic, face masks were widely adopted for viral mitigation and remain crucial public health tools. However, questions persist about their impact on emotion recognition and neural processing, especially in children, parents, and young adults. We developed the Masked Affective and Social Cognition task, featuring masked and unmasked faces displaying fear, sadness, and anger. We recruited three racial and ethnically diverse samples: 119 college students, 30 children who entered school age at the beginning of the pandemic, and 31 fathers of the aforementioned children. Of the latter two groups, 41 participants (n = 23 fathers, 18 children) did the Masked Affective and Social Cognition task during a neuroimaging scan, while the remaining 20 participants (n = 8 fathers, 12 children) who were not eligible for scanning completed the task during their lab visit. Behaviorally, we found that participants recognized emotions less accurately when viewing masked faces and also found an interaction of emotion by condition, such that accuracy was particularly compromised by sad masked faces. Neurally, masked faces elicited greater activation in the posterior cingulate, insula, and fusiform gyrus. Anterior insula and inferior frontal gyrus activation were driven by sad, masked faces. These results were consistent across age groups. Among fathers, activation to sad masked faces was associated with stress and depression. Overall, our findings did not depend on previous mask exposure or timing of participation during the pandemic. These results have implications for understanding face emotion recognition, empathy, and socioemotional neurodevelopment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Genetic and Neuronal Basis for Facial Emotion Perception in Humans and Macaques

Abstract The ability to rapidly recognize basic facial emotions (e.g., fear) is crucial for social interactions and adaptive functioning. To date, the origin of facial emotion recognition ability remains equivocal. Using a classical twin design in humans, we found a clear dissection of low and high spatial frequencies (LSF and HSF) in facial emotion perception: whereas genetic factors contributed to individual variation in LSF processing, HSF processing is largely shaped by environmental effects. Furthermore, the ability to recognize facial emotion of LSF content genetically correlated with the function of the amygdala. Crucially, single-unit recording of the amygdala in macaques further revealed the dissociation between LSF and HSF processing in facial emotion perception, indicating the existence of an evolutionarily conserved mechanism. This cross-species study enhances insights into the neurobiological dual-route model (subcortical vs. cortical) of emotion perception and illuminates the origin and the functional development of the emotional brain in primates.

Disgust-specific impairment of facial emotion recognition in Parkinson's disease patients with mild cognitive impairment.

This study investigated the association between cognitive function and facial emotion recognition (FER) in patients with Parkinson's disease (PD) and mild cognitive impairment (PD-MCI). We enrolled 126 participants from Taiwan, including 63 patients with idiopathic PD and 63 matched healthy controls. The PD group was divided into two groups: those with normal cognitive function (PD-NC) and those with MCI (PD-MCI). Participants underwent a modality emotion recognition test and comprehensive cognitive assessment. Our findings reveal that patients with PD-MCI exhibit significantly impaired FER, especially in recognizing "disgust," compared with patients with PD-NC and healthy adults (P = .001). This deficit correlates with executive function, attention, memory, and visuospatial abilities. Attention mediates the relationship between executive function and "disgust" FER. The findings highlight how patients with PD-MCI are specifically challenged when recognizing "disgust" and suggest that cognitive training focusing on cognitive flexibility and attention may improve their FER abilities. This study contributes to our understanding of the nuanced relationship between cognitive dysfunction and FER in patients with PD-MCI, emphasizing the need for targeted interventions.

The impact of fear of pain on the temporal processing of emotional faces: modulation of attentional resources.

Recent research has focused on the spatial attention bias toward pain-related cues in individuals with fear of pain (FOP), but our understanding of its temporal attention features, particularly regarding emotional faces, is limited. To address this gap, 39 individuals with high fear of pain (H-FOP) and 37 with low fear of pain (L-FOP) completed a rapid serial visual presentation dual task. Participants viewed a series of rapidly presented pictures, first identified neutral building images as the first target (T1) and then emotional faces (painful, neutral, and happy) as the second target (T2). T1 and T2 presented at time intervals of 232ms (lag 2) and 696ms (lag 6), respectively. Results indicate that H-FOP group members were less accurate in recognizing faces at lag 2 shortly after correctly identifying T1 compared to those L-FOP group members. Notably, all participants were more accurate in recognizing painful faces at lag 2 than in identifying neutral and happy faces, suggesting a negative bias toward pain stimuli when attentional resources were limited. In contrast, at lag 6, when attentional resources were more abundant, participants showed greater accuracy in recognizing neutral faces than emotional faces. These findings underscore the influence of fear of pain and available cognitive resources on the attentional processing of emotional faces over time, providing insights into how fear of pain affects the temporal dynamics of emotional face recognition and the role of attentional resources.

Impairments in facial expression recognition in patients with euthymic bipolar disorders using the facial emotions recognition test

BackgroundPatients with bipolar disorders (BD) have significant impairments in Facial Expression Recognition (FER), an essential social skill for effective social interactions. While the Facial Emotions Recognition Test, 54 photographs by Gaudelus (TREF-54g) has been used in patients with schizophrenia, no study has evaluated FER using this test in BD patients. The TREF-54g meets the International Society for Bipolar Disorders (ISBD) criteria for assessing FER. The objective of this study was to compare FER in patients with euthymic BD versus healthy controls (HC) using the TREF-54g. MethodsThis study included 60 patients with BD diagnosed according to DSM-5 criteria, 30 with bipolar I disorder (BDI) and 30 with bipolar II disorder (BDII) and 60 HC. All groups were matched for age, gender, and years of education. Overall TREF-54g impairment score was defined as a difference of two SD from the group of HC with mean overall accuracy score expressed as a percentage of correct responses. ResultsOverall TREF-54g impairment scores, as well as sub-scores for anger and contempt recognition, were significantly higher in patients with BD versus those in HC. No significant differences in TREF-54g scores were shown between patients with BDI and those with BDII. LimitationsClinical assessments of this study didn't include neurocognition assessments and classification of contempt emotion as a basic emotion remains controversial. ConclusionsWe found significant impairments in FER in patients with euthymic BD, particularly for anger and contempt using the TREF-54g a test that meets ISBD recommendation criteria.

An improved student’s facial emotions recognition method using transfer learning

An improved student’s facial emotions recognition method using transfer learning

Facial emotion recognition using deep quantum and advanced transfer learning mechanism.

Facial expressions have become a common way for interaction among humans. People cannot comprehend and predict the emotions or expressions of individuals through simple vision. Thus, in psychology, detecting facial expressions or emotion analysis demands an assessment and evaluation of decisions for identifying the emotions of a person or any group during communication. With the recent evolution of technology, AI (Artificial Intelligence) has gained significant usage, wherein DL (Deep Learning) based algorithms are employed for detecting facial expressions. The study proposes a system design that detects facial expressions by extracting relevant features using a Modified ResNet model. The proposed system stacks building-blocks with residual connections and employs an advanced extraction method with quantum computing, which significantly reduces computation time compared to conventional methods. The backbone stem utilizes a quantum convolutional layer comprised of several parameterized quantum-filters. Additionally, the research integrates residual connections in the ResNet-18 model with the Modified up Sampled Bottle Neck Process (MuS-BNP), retaining computational efficacy while benefiting from residual connections. The proposed model demonstrates superior performance by overcoming the issue of maximum similarity within varied facial expressions. The system's ability to accurately detect and differentiate between expressions is measured using performance metrics such as accuracy, F1-score, recall, and precision. This performance analysis confirms the efficacy of the proposed system, highlighting the advantages of quantum computing in feature extraction and the integration of residual connections. The model achieves quantum superiority, providing faster and more accurate computations compared to existing methodologies. The results suggest that the proposed approach offers a promising solution for facial expression recognition tasks, significantly improving both speed and accuracy.

Enhancing dance education through convolutional neural networks and blended learning

This article explores the evolving landscape of dance teaching, acknowledging the transformative impact of the internet and technology. With the emergence of online platforms, dance education is no longer confined to physical classrooms but can extend to virtual spaces, facilitating a more flexible and accessible learning experience. Blended learning, integrating traditional offline methods and online resources, offers a versatile approach that transcends geographical and temporal constraints. The article highlights the utilization of the dual-wing harmonium (DWH) multi-view metric learning (MVML) algorithm for facial emotion recognition, enhancing the assessment of students’ emotional expression in dance performances. Moreover, the integration of motion capture technology with convolutional neural networks (CNNs) facilitates a precise analysis of students’ dance movements, offering detailed feedback and recommendations for improvement. A holistic assessment of students’ performance is attained by combining the evaluation of emotional expression with the analysis of dance movements. Experimental findings support the efficacy of this approach, demonstrating high recognition accuracy and offering valuable insights into the effectiveness of dance teaching. By embracing technological advancements, this method introduces novel ideas and methodologies for objective evaluation in dance education, paving the way for enhanced learning outcomes and pedagogical practices in the future.

Impact of Bell's palsy in facial emotion recognition: a prospective observational study of 60 patients.

Patients with Bell's palsy suffer from functional deficits and cannot convey their emotions through the face as well as they used to. According to embodied cognition, automatic mimicry and facial feedback modulate emotion perception. The aim of our study was to determine the impact of Bell's palsy on facial emotion perception. Facial motor skills, the use of botulinum toxin injections, and anxiety were studied. 60 patients completed Emotest-VA, an assessment tool of facial emotion perception, facial motor assessment and affective questionnaires. Facial perception scores of patients were compared to the normative data provided by Emotest-VA. Relationships between facial motor skills, anxiety and perceptual abilities were carried out with Pearson's correlations. Perception accuracy scores were abnormal in 12% of patients. Patients were more ambiguous than normative population to perceive emotion (t(23) = 4.14, p < .001). Happiness arousal was negatively correlated to smile asymmetry (n = 60, r=-.294 p = .022). Patients who received botulinum toxin were more accurate to perceive disgust (Z = 3.60, p < .001). State anxiety decreased when patients could improve their horizontal smile (r = - 440, p < .001). Bell's palsy impaired sensorimotor simulation, thereby reducing emotional contagion. Smile measures could indicate patients' recovery and patients' anxiety. It could also predict patient's perceptual abilities. These results provide new directions in the management of Bell's palsy. Additionally to physical therapy, it is relevant to improve emotional contagion both in its productive and perceptive aspects through facial feedback.

Children with facial differences experience deficits in emotion skills.

Children with facial differences, such as repaired cleft lip and palate (CLP), may present with reduced capacity for sensorimotor simulation, particularly in the form of facial mimicry. This study examined whether facial mimicry, emotion recognition, and empathy skills are reduced in children with CLP when compared with sex/age-matched controls. A case-control design was utilized. Forty-five children between the ages of 8 and 12 with CLP, and 45 age/sex-matched controls were recruited. Participants completed a facial mimicry task, and facial movements were tracked and quantified using OpenFace. Participants also completed picture and context-based emotion recognition tasks. Picture-based assessment involved identifying emotions from the Dynamic FACES database. Context-based assessment consisted of identifying how a child might feel in various situations. Finally, participants and their parents completed the Empathy Questionnaire for Children and Adolescents (EmQue-CA). Children with CLP presented with significantly reduced facial mimicry (p = .017), picture-based (p < .001), and context-based emotion recognition scores (p < .001) when compared with controls. Better facial mimicry was associated with better picture-based emotion recognition scores in the control group only (r = .22, p < .01). Children with CLP also had significantly lower child and parent-proxy EmQue-CA scores (p < .001). Greater facial mimicry significantly predicted better parent-proxy EmQue-CA scores (p = .016) but did not predict child scores. Children with CLP presented with reduced facial mimicry, poorer emotion recognition, and empathy skills. These findings have a broader relevance as they suggest children with facial differences may present with reduced facial mimicry and/or deficits in emotion recognition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A facial and vocal emotion recognition system based on deep learning

The traditional teaching mode, mainly characterized by "cramming" instruction, often overlooks the absorption of knowledge by students during the teaching process. Even in recent years, the emerging "flipped classroom" model heavily relies on teachers' experience and expertise, making it challenging to quantify students' classroom learning states. Among young teachers lacking teaching experience, the replication of excellent teaching models faces difficulties. With the rapid development of artificial intelligence technology, deep learning and other AI techniques are increasingly applied to people's lives. Additionally, emotion computing technology is also becoming more sophisticated. In the field of education, the use of AI technology holds the potential to help teachers systematically and comprehensively assess teaching quality, promoting the sharing of high-quality educational resources nationally and globally. Existing emotion detection technologies often focus on single techniques such as image recognition or natural language processing, resulting in low accuracy in discerning human emotions and making it difficult to accurately identify emotional changes in complex scenarios such as classrooms. Furthermore, there is a lack of comprehensive emotional statistical analysis methods for audio and visual data. In light of these, we have designed and implemented a multi-modal emotion recognition and analysis system. Through testing in classroom settings, this system has the potential to assist classroom teaching and enhance teaching quality.

Evaluating the Impact of BoNT-A Injections on Facial Expressions: A Deep Learning Analysis.

Botulinum Toxin Type A (BoNT-A) injections are widely used for facial rejuvenation, but their effects on facial expressions remain unclear. This study aims to objectively measure the impact of BoNT-A injections on facial expressions using deep learning techniques. 180 patients aged 25-60 years who underwent BoNT-A application to the upper face were included. Patients were photographed with neutral, happy, surprised, and angry expressions before and 14 days after the procedure. A Convolutional Neural Network (CNN)-based Facial Emotion Recognition (FER) system analyzed 1440 photographs using a hybrid dataset of clinical images and the Karolinska Directed Emotional Faces (KDEF) dataset. The CNN model accurately predicted 90.15% of the test images. Significant decreases in the recognition of angry and surprised expressions were observed post-injection (p<0.05), with no significant changes in happy and neutral expressions (p>0.05). Angry expressions were often misclassified as neutral or happy (p<0.05), and surprised expressions were more likely to be perceived as neutral (p<0.05). Deep learning can effectively assess the impact of BoNT-A injections on facial expressions, providing more standardized data than traditional surveys. BoNT-A may reduce the expression of anger and surprise, potentially leading to a more positive facial appearance and emotional state. Further studies are needed to understand the broader implications of these changes.

Effects of Sleep Quality, Acute Sleep Deprivation, and Napping on Facial Emotion Recognition Accuracy and Speed.

To investigate the effects of sleep quality, sleep deprivation, and napping on facial emotion recognition (FER) accuracy and speed. This research included a cross-sectional study (102 qualified participants) and a randomized controlled study (26 in the napping group and 24 in the control group). The stimuli for the FER task were obtained from the Chinese Facial Affective Picture System (CFAPS). Four facial expressions (fearful, disgusted, sad, and angry) were used. The Pittsburgh Sleep Quality Index (PSQI), Self-Rating Anxiety Scale, and Self-Rating Depression Scale were used to measure participants' sleep quality and psychological conditions. In Study 1, FER ability was compared between good and poor sleepers. In Study 2, all participants were sleep-deprived for one night, and completed the FER task before and after sleep deprivation. After different interventions (ie, napping for one hour, or walking around for ten minutes), the participants completed the third FER task. Study 1: Poor sleepers were able to recognize sad expressions more accurately compared with good sleepers. Study 2: 30-h sleep deprivation had no significant effect on the accuracy (ACC). Napping after sleep deprivation improved the FER ACC of upper-face expressions and marginally significantly improved the FER ACC of disgusted expressions. Better sleep quality was linked to lower FER accuracy, particularly in recognizing sad expressions, while no significant differences in recognition speed were observed. Additionally, 30hours of sleep deprivation did not affect FER accuracy, but napping after sleep deprivation improved accuracy for upper-face and marginally for disgusted expressions.

Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

Facial expression recognition (FER) plays a pivotal role in various applications, ranging from human-computer interaction to psychoanalysis. To improve the accuracy of facial emotion recognition (FER) models, this study focuses on enhancing and augmenting FER datasets. It comprehensively analyzes the Facial Emotion Recognition dataset (FER13) to identify defects and correct misclassifications. The FER13 dataset represents a crucial resource for researchers developing Deep Learning (DL) models aimed at recognizing emotions based on facial features. Subsequently, this article develops a new facial dataset by expanding upon the original FER13 dataset. Similar to the FER + dataset, the expanded dataset incorporates a wider range of emotions while maintaining data accuracy. To further improve the dataset, it will be integrated with the extended Cohn-Kanade (CK+) dataset.This paper investigates the application of modern DL models to enhance emotion recognition in human faces. By training a new dataset, the study demonstrates significant performance gains compared with its counterparts. Furthermore, the article examines recent advances in FER technology and identifies critical requirements for DL models to overcome the inherent challenges of this task effectively. The study explores several DL architectures for emotion recognition in facial image datasets, with a particular focus on convolutional neural networks (CNNs). Our findings indicate that complex architecture, such as EfficientNetB7, outperforms other DL architectures, achieving a test accuracy of 78.9 %. Notably, the model surpassed the EfficientNet-XGBoost model, especially when used with the new dataset. Our approach leverages EfficientNetB7 as a backbone to build a model capable of efficiently recognizing emotions from facial images. Our proposed model, EfficientNetB7-CNN, achieved a peak accuracy of 81 % on the test set despite facing challenges such as GPU memory limitations. This demonstrates the model's robustness in handling complex facial expressions. Furthermore, to enhance feature extraction and attention mechanisms, we propose a new hybrid model, CBAM-4CNN, which integrates the convolutional block attention module (CBAM) with a custom 4-layer CNN architecture. The results showed that the CBAM-4CNN model outperformed existing models, achieving higher accuracy, precision, and recall metrics across multiple emotion classes. The results highlight the critical role of comprehensive and diverse data in enhancing model performance for facial emotion recognition.

Efficacy of a Culturally Adapted Emotion Recognition Training Program in Improving Facial Emotion Recognition in Persons with Schizophrenia

Background: Facial emotion recognition (FER) deficits are being considered as core features of social cognitive deficits in schizophrenia, associated with socio-occupational dysfunction. Social cognition intervention programs have been shown to improve these deficits; however, there are no such intervention strategies in India. In this study, we aim to examine the efficacy of Training of Affect Recognition – Indian Version (TAR-IV) to enhance the FER abilities of people with schizophrenia. Methods: In an open-label experimental design, 36 participants with schizophrenia underwent 12 sessions of TAR-IV as an add-on to treatment as usual (TAU), while 29 participants with schizophrenia continued TAU (pharmacological treatment with or without occupational therapy and vocational rehabilitation services). Clinical and functional assessments were done using the positive and negative syndrome scale (PANSS) and socio-occupational functioning scale. Emotion recognition abilities were assessed on the tool for recognition of emotions in neuropsychiatric disorders at baseline, post-intervention, and follow-up (two to three months post-intervention). Results: The intervention group showed significant improvements in FER ( P = .001) and socio-occupational functioning ( P = .008) after receiving the TAR-IV, which remained significant at two months follow-up. A lower age of onset and poorer neurocognitive function at baseline predicted greater changes in emotion recognition ability following the intervention. Conclusions: This study demonstrated the efficacy of TAR-IV, the Indian adaptation of social cognition intervention, in improving emotion recognition abilities and socio-occupational functioning in patients with schizophrenia. These findings need to be replicated in randomized controlled trials.

Decoding the silent language: A framework for advanced visual emotion recognition to enhance worker well-being on IIoT-enabled manufacturing

The Industrial Internet of Things (IIoT), pivotal in the Industry 4.0 revolution, has transformed manufacturing, particularly assembly lines, by enabling real-time process monitoring and data analysis to enhance productivity. Yet, its potential impact on worker well-being has been less explored. As the National Association of Manufacturers anticipates an opening of 2.1 million unfilled jobs by 2030, the necessity for frameworks that not only understand but also improve employee well-being becomes apparent. We introduce a novel, non-intrusive visual emotion recognition framework that incorporates facial emotion recognition (FER) and emotional body posture recognition (EBPR) to assess workers’ psychological states. This initiative aims to mitigate human errors and accidents linked to stress or high cognitive load, thereby enhancing the manufacturing environment. Strategies such as optimized break time planning, line balancing, procedure modifications, and improved training are proposed to ensure both employee well-being and stakeholder benefits. This framework indirectly supports the industry’s efforts to address the looming challenge of unfilled jobs by fostering a supportive work environment that enhances job satisfaction, potentially making positions more appealing and retaining valuable talent. By bridging the gap between technological advancements and human-centric considerations, our approach underscores the significance of integrating IIoT solutions with employee welfare initiatives to address both productivity and workforce sustainability challenges.

Emotional scenes as context in emotional expression recognition: The role of emotion or valence match.

Emotion recognition is influenced by contextual information such as social category cues or background scenes. However, past studies yielded mixed findings regarding whether broad valence or specific emotion matches drive context effects and how multiple sources of contextual information may influence emotion recognition. To address these questions, participants were asked to categorize expressions on male and female faces posing happiness and anger and happiness and fear on pleasant and fearful backgrounds (Experiment 1, conducted in 2019), fearful and disgusted expressions on fear and disgust eliciting backgrounds (Experiment 2, conducted in 2022), and fearful and sad expressions on fear and sadness eliciting backgrounds (Experiment 3, conducted in 2022). In Experiment 1 (where stimuli varied in valence), a broad valence match effect was observed. Faster recognition of happiness than fear and anger was more pronounced in pleasant compared to fearful scenes. In Experiments 2 and 3 (where stimuli were negative in valence), specific emotion match effects were observed. Faster recognition occurred when expression and background were emotionally congruent. In Experiments 1 and 3, poser sex independently moderated emotional expression recognition speed. These results suggest that the effect of emotional scenes on facial emotion recognition is mediated by a match in valence when broad valence is task-relevant. Specific emotion matches drive context effects when participants categorize expressions of a single valence. Looking at the influence of background contexts and poser sex together suggests that these two sources of contextual information have an independent rather than an interactive influence on emotional expression recognition speed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).