Facial Emotion Recognition Task Research Articles

A study on expression recognition based on improved mobilenetV2 network

This paper proposes an improved strategy for the MobileNetV2 neural network(I-MobileNetV2) in response to problems such as large parameter quantities in existing deep convolutional neural networks and the shortcomings of the lightweight neural network MobileNetV2 such as easy loss of feature information, poor real-time performance, and low accuracy rate in facial emotion recognition tasks. The network inherits the characteristics of MobilenetV2 depthwise separated convolution, signifying a reduction in computational load while maintaining a lightweight profile. It utilizes a reverse fusion mechanism to retain negative features, which makes the information less likely to be lost. The SELU activation function is used to replace the RELU6 activation function to avoid gradient vanishing. Meanwhile, to improve the feature recognition capability, the channel attention mechanism (Squeeze-and-Excitation Networks (SE-Net)) is integrated into the MobilenetV2 network. Experiments conducted on the facial expression datasets FER2013 and CK + showed that the proposed network model achieved facial expression recognition accuracies of 68.62% and 95.96%, improving upon the MobileNetV2 model by 0.72% and 6.14% respectively, and the parameter count decreased by 83.8%. These results empirically verify the effectiveness of the improvements made to the network model.

Congenital Anosmia and Facial Emotion Recognition

Major functions of the olfactory system include guiding ingestion and avoidance of environmental hazards. People with anosmia report reliance on others, for example to check the edibility of food, as their primary coping strategy. Facial expressions are a major source of non-verbal social information that can be used to guide approach and avoidance behaviour. Thus, it is of interest to explore whether a life-long absence of the sense of smell heightens sensitivity to others’ facial emotions, particularly those depicting threat. In the present, online study 28 people with congenital anosmia (mean age 43.46) and 24 people reporting no olfactory dysfunction (mean age 42.75) completed a facial emotion recognition task whereby emotionally neutral faces (6 different identities) morphed, over 40 stages, to express one of 5 basic emotions: anger, disgust, fear, happiness, or sadness. Results showed that, while the groups did not differ in their ability to identify the final, full-strength emotional expressions, nor in the accuracy of their first response, the congenital anosmia group successfully identified the emotions at significantly lower intensity (i.e. an earlier stage of the morph) than the control group. Exploratory analysis showed this main effect was primarily driven by an advantage in detecting anger and disgust. These findings indicate the absence of a functioning sense of smell during development leads to compensatory changes in visual, social cognition. Future work should explore the neural and behavioural basis for this advantage.

Dynamic facial emotion recognition and affective prosody recognition are associated in patients with temporal lobe epilepsy

Deficits in facial emotion recognition have frequently been established in temporal lobe epilepsy (TLE). However, static, rather than dynamic emotion recognition paradigms have been applied. Affective prosody has been insufficiently studied in TLE, and there is a lack of studies investigating associations between auditory and visual emotion recognition. We wished to investigate potential deficits in a dynamic morph task of facial emotion recognition and in an affective prosody recognition task, as well as associations between both tasks. 25 patients with TLE and 24 healthy controls (CG) performed a morph task with faces continuously changing in their emotional intensity. They had to press a button, as soon as they were able to recognize the emotion expressed, and label it accordingly. In the auditory task, subjects listened to neutral sentences spoken in varying emotional tones, and labeled the emotions. Correlation analyses were conducted across both tasks. TLE patients showed significantly reduced prosody recognition compared to CG, and in the morph task, there was a statistical trend towards significantly reduced performance for TLE. Recognition rates in both tasks were significantly associated. TLE patients show deficits in affective prosody recognition, and they may also be impaired in a morph task with dynamically changing facial expressions. Impairments in basic social-cognitive tasks in TLE seem to be modality-independent.

Disrupted properties of functional brain networks in major depressive disorder during emotional face recognition: an EEG study via graph theory analysis.

Previous neuroimaging studies have revealed abnormal brain networks in patients with major depressive disorder (MDD) in emotional processing. While any cognitive task consists of a series of stages, little is yet known about the topology of functional brain networks in MDD for these stages during emotional face recognition. To address this problem, electroencephalography (EEG)-based functional brain networks of MDD patients at different stages of facial information processing were investigated in this study. First, EEG signals were collected from 16 patients with MDD and 18 age-, gender-, and education-matched normal subjects when performing an emotional face recognition task. Second, the global field power (GFP) method was employed to divide group-averaged event-related potentials into different stages. Third, using the phase transfer entropy (PTE) approach, the brain networks of MDD patients and normal individuals were constructed for each stage in negative and positive face processing, respectively. Finally, we compared the topological properties of brain networks of each stage between the two groups using graph theory approaches. The results showed that the analyzed three stages of emotional face processing corresponded to specific neurophysiological phases, namely, visual perception, face recognition, and emotional decision-making. It was also demonstrated that depressed patients showed abnormally decreased characteristic path length at the visual perception stage of negative face recognition and normalized characteristic path length in the stage of emotional decision-making during positive face processing compared to healthy subjects. Furthermore, while both the MDD and normal groups' brain networks were found to exhibit small-world network characteristics, the brain network of patients with depression tended to be randomized. Moreover, for patients with MDD, the centro-parietal region may lose its status as a hub in the process of facial expression identification. Together, our findings suggested that altered emotional function in MDD patients might be associated with disruptions in the topological organization of functional brain networks during emotional face recognition, which further deepened our understanding of the emotion processing dysfunction underlying MDD.

Deep Learning Approaches for Classification of Emotion Recognition based on Facial Expressions

Automated emotion recognition (AEE) plays a crucial role in numerous industries that depend on understanding human emotional responses, such as advertising, technology, and human-robot interaction, particularly within the Information Technology (IT) field. However, current systems can often come up short in comprehensively understanding an individual's emotions, as prior research has mainly focused on assessing facial expressions and categorizing them into seven primary emotions, including neutrality. In this study, we present several Deep Convolutional Neural Network (CNN) models designed specifically for the task of facial emotion recognition, utilizing the FER2013 and RAF datasets. The baseline CNN model is established through a trial-and-error method, and its results are compared with more complex deep learning techniques, including ResNet18, VGGNet16, VGGNet19, and Efficient Net-B0 models. Among these models, the VGGNet19 model achieved the best results with a test accuracy of 71.02% on the FER2013 dataset. In comparison, the ResNet18 model outperformed all other models with an 86.02% test accuracy on the RAF-DB dataset. These results underscore the potential for advancing automated emotion recognition through complex deep-learning techniques.

Multi-source transfer learning for facial emotion recognition using multivariate correlation analysis.

Deep learning techniques have proven to be effective in solving the facial emotion recognition (FER) problem. However, it demands a significant amount of supervision data which is often unavailable due to privacy and ethical concerns. In this paper, we present a novel approach for addressing the FER problem using multi-source transfer learning. The proposed method leverages the knowledge from multiple data sources of similar domains to inform the model on a related task. The approach involves the optimization of aggregate multivariate correlation among the source tasks trained on the source dataset, thus controlling the transfer of information to the target task. The hypothesis is validated on benchmark datasets for facial emotion recognition and image classification tasks, and the results demonstrate the effectiveness of the proposed method in capturing the group correlation among features, as well as being robust to negative transfer and performing well in few-shot multi-source adaptation. With respect to the state-of-the-art methods MCW and DECISION, our approach shows an improvement of 7% and [Formula: see text]15% respectively.

Supraphysiological testosterone levels from anabolic steroid use and reduced sensitivity to negative facial expressions in men

RationaleAnabolic-androgenic steroids (AAS) are used to improve physical performance and appearance, but have been associated with deficits in social cognitive functioning. Approximately 30% of people who use AAS develop a dependence, increasing the risk for undesired effects.ObjectivesTo assess the relationship between AAS use (current/previous), AAS dependence, and the ability to recognize emotional facial expressions, and investigate the potential mediating role of hormone levels.MethodsIn total 156 male weightlifters, including those with current (n = 45) or previous (n = 34) AAS use and never-using controls (n = 77), completed a facial Emotion Recognition Task (ERT). Participants were presented with faces expressing one out of six emotions (sadness, happiness, fear, anger, disgust, and surprise) and were instructed to indicate which of the six emotions each face displayed. ERT accuracy and response time were recorded and evaluated for association with AAS use status, AAS dependence, and serum reproductive hormone levels. Mediation models were used to evaluate the mediating role of androgens in the relationship between AAS use and ERT performance.ResultsCompared to never-using controls, men currently using AAS exhibited lower recognition accuracy for facial emotional expressions, particularly anger (Cohen’s d = −0.57, pFDR = 0.03) and disgust (d = −0.51, pFDR = 0.05). Those with AAS dependence (n = 47) demonstrated worse recognition of fear relative to men without dependence (d = 0.58, p = 0.03). Recognition of disgust was negatively correlated with serum free testosterone index (FTI); however, FTI did not significantly mediate the association between AAS use and recognition of disgust.ConclusionsOur findings demonstrate impaired facial emotion recognition among men currently using AAS compared to controls. While further studies are needed to investigate potential mechanisms, our analysis did not support a simple mediation effect of serum FTI.

Effects of two different social exclusion paradigms on ambiguous facial emotion recognition

ABSTRACT Social exclusion is an emotionally painful experience that leads to various alterations in socio-emotional processing. The perceptual and emotional consequences that may arise from experiencing social exclusion can vary depending on the paradigm used to manipulate it. Exclusion paradigms can vary in terms of the severity and duration of the leading exclusion experience, thereby classifying it as either a short-term or long-term experience. The present study aimed to study the impact of exclusion on socio-emotional processing using different paradigms that caused experiencing short-term and imagining long-term exclusion. Ambiguous facial emotions were used as socio-emotional cues. In study 1, the Ostracism Online paradigm was used to manipulate short-term exclusion. In study 2, a new sample of participants imagined long-term exclusion through the future life alone paradigm. Participants of both studies then completed a facial emotion recognition task consisting of morphed ambiguous facial emotions. By means of Point of Subjective Equivalence analyses, our results indicate that the experience of short-term exclusion hinders recognising happy facial expressions. In contrast, imagining long-term exclusion causes difficulties in recognising sad facial expressions. These findings extend the current literature, suggesting that not all social exclusion paradigms affect socio-emotional processing similarly.

Comparative Analysis of Vision Transformer Models for Facial Emotion Recognition Using Augmented Balanced Datasets

Facial emotion recognition (FER) has a huge importance in the field of human–machine interface. Given the intricacies of human facial expressions and the inherent variations in images, which are characterized by diverse facial poses and lighting conditions, the task of FER remains a challenging endeavour for computer-based models. Recent advancements have seen vision transformer (ViT) models attain state-of-the-art results across various computer vision tasks, encompassing image classification, object detection, and segmentation. Moreover, one of the most important aspects of creating strong machine learning models is correcting data imbalances. To avoid biased predictions and guarantee reliable findings, it is essential to maintain the distribution equilibrium of the training dataset. In this work, we have chosen two widely used open-source datasets, RAF-DB and FER2013. As well as resolving the imbalance problem, we present a new, balanced dataset, applying data augmentation techniques and cleaning poor-quality images from the FER2013 dataset. We then conduct a comprehensive evaluation of thirteen different ViT models with these three datasets. Our investigation concludes that ViT models present a promising approach for FER tasks. Among these ViT models, Mobile ViT and Tokens-to-Token ViT models appear to be the most effective, followed by PiT and Cross Former models.

Pseudo-labeling of transfer learning convolutional neural network data for human facial emotion recognition

The relevance of solving the problem of facial emotion recognition on human images in the creation of modern intelligent systems of computer vision and human-machine interaction, online learning and emotional marketing, health care and forensics, machine graphics and game intelligence is shown. Successful examples of technological solutions to the problem of facial emotion recognition using transfer learning of deep convolutional neural networks are shown. But the use of such popular datasets as DISFA, CelebA, AffectNet, for deep learning of convolutional neuralnetworks does not give good results in terms of the accuracy of emotion recognition, because almost all training sets have fundamental flaws related to errors in their creation, such as the lack of data of a certain class, imbalance of classes, subjectivity and ambiguity of labeling, insufficient amount of data for deep learning, etc. It is proposed to overcome the noted shortcomings of popular datasets for emotion recognition by adding to the training sample additional pseudo-labeled images with human emotions, on which recognition occurs with high accuracy. The aim of the research is to increase the accuracy of facial emotion recognitionon the image of a human by developing a pseudo-labeling method for transfer learning of a deep neural network. To achieve the aim, the following tasks were solved: a convolutional neural network model, previously trained on the ImageNet set using the transfer learning method, was adjusted on the RAF-DB data set to solve emotion recognition tasks; a pseudo-labeling method of the RAF−DB set data was developed for semi-supervised learning of a convolutional neural network model for the task of facial emotion recognition; the accuracy of facial emotion recognition was analyzed based on the developed convolutional neural network model and the method of pseudo-labeling of RAF-DB set data for its correction. It is shown that the use of the developed method of pseudo-labeling data and transfer learning of the MobileNet V1 convolutional neural network model allowed to increase the accuracy of facial emotion recognitionon the images of the RAF-DB dataset by 2 percent (from 76 to 78%) according to the F1 estimate. Atthe same time, taking into account the significant imbalance of the classes, for the 7 main emotions in the trainingset, we have a significant increase in the accuracy of recognizing a few representatives of such emotions as surprise (from 71 to 77%), fearful(from 64 to 69%), sad (from 72 to 76%), angrywith (from 64 to 74%), neutral(from 66 to 71%). The accuracy of recognizing the emotion of happy, which is the most common, decreased (from 91 to 86 %) Thus, it can be concluded that the use of the developed pseudo-labeling method gives good results in overcoming such shortcomings of datasets for deep learning of convolutional neural networks such as lack of data of a certain type, imbalance of classes, insufficient amount of data for deep learning, etc.

Sex Differences in Recognition of Face Expressions

Abstract: Emotional facial expressions are a crucial non-verbal communication skill for humans’ interactions. The current study assessed the impact of sex on emotional face recognition. A total of 125 individuals per-formed an online emotional face recognition task. The stimuli were created recutting male and female Saudi volunteers. Results showed that, except of the male participants having faster responses compared to females in general, no main significant differences between the sex group in accuracy nor any significant interaction between participant sex and the sex of the faces. Our findings suggests that the effect of sex on emotional face recognition needs further investigation with well calibrated stimuli. Limitations and the direction of future research in this area were discussed.

Elevated EEG heartbeat-evoked potentials in adolescents with more ADHD symptoms

IntroductionSymptoms of attention deficit hyperactivity disorder (ADHD) are associated with a variety of mental abnormalities, but little is known about the perception and processing of internal signals, i.e., interoception, in individuals with ADHD symptoms. This study aimed to investigate the association between ADHD symptoms and the heartbeat-evoked potential (HEP), known as a neural correlate of automatic interoceptive processing of cardiac signals, in adolescents. MethodsHEPs of 47 healthy adolescent participants (53.2 % female) with a mean age of 14.29 years were measured during an emotional face recognition task. In addition, participants completed a self-report screening for ADHD symptoms. ResultsADHD symptoms were positively related to the HEP activity during the task in three of eight EEG sectors in the left hemisphere, as well as in all sectors in the right hemisphere. DiscussionThis study is the first to demonstrate preliminary a relationship between the strength of HEP activity and ADHD symptoms in awake subjects. This finding of higher HEP amplitudes in subjects with more ADHD symptoms can be interpreted in terms of (i) increased arousal, (ii) altered neural processing of internal processes in an emotion-relevant task, and (iii) a misaligned precision-weighting process of task-irrelevant stimuli according to the predictive coding framework. These different interpretations could be reflected by previous studies showing heterogeneity of psychological deficits in individuals with ADHD symptoms. However, the generalizability to patients with diagnosed ADHD is limited due to the measurement tool for ADHD symptoms and the sample characteristics.

Gender Differences in Facial Emotion Recognition Among Adolescents Depression with Non-Suicidal Self-Injury.

Despite the perception that healthy female are superior at emotional identification, it remains unclear whether gender-specific differences exist in adolescent depression and whether such specific differences in emotional recognition are associated with the most salient feature of adolescent depression---non-suicidal self-injury (NSSI). In this study, 1428 adolescents (1136 females and 292 males) with depression and NSSI were examined using the Facial Emotion Recognition Task, Patient Health Questionnaire-9 (PHQ-9), and Functional Assessment of Self-mutilation questionnaire (FASM). This study was grouped by gender. Data were analyzed using the descriptive statistics, independent sample t-test, chi-square test, non-parametric test (Mann-Whitney U-test), Spearman correlation and Multiple linear regression analysis. Depressed females reported a significantly greater frequency of self-injurious behaviour and more severe depressive symptoms than males. Face emotion recognition was also significantly more accurate in females and was positively correlated with levels of self-injury and depression, whereas no such correlations were found in males. Among depressed adolescents, face emotion recognition is better in females and is associated with self-injurious behaviour. This study found that the greater susceptibility to depression and NSSI among adolescent females may stem in part from superior recognition and sensitivity to the negative emotions of others.

Maternal recognition of positive emotion predicts sensitive parenting in infancy.

Research on parent-child relationships demonstrates the importance of maternal sensitivity for the development of children's emotion regulation, social competence, and health; thus, it is important to understand the emotional-cognitive capacities underlying maternal sensitivity. We followed 120 mothers and their full-term infants from the newborn period to 5 months postpartum. Mothers' emotion recognition during the newborn period was measured using a validated facial emotion recognition task assessing discrimination (d') of six facial expressions of emotion: happiness, fear, anger, sadness, disgust, and neutrality. Maternal behavior at 5 months postpartum was coded from a mother-infant free-play session using Ainsworth's Sensitivity Scales. Preregistered analyses revealed that mothers' ability to detect happiness specifically (but not other emotions such as fear or sadness) in the neonatal period predicted greater observed sensitivity 4 months later, β = .30, p = .002, ΔR² = .08. Results suggest that maternal recognition of positive emotion may be uniquely predictive of sensitive behavior in low-stress parent-infant interaction contexts. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Facial emotion recognition processes according to schizotypal personality traits: An eye-tracking study

Facial emotion recognition has been shown to be impaired among patients with schizophrenia and, to a lesser extent, among individuals with high levels of schizotypal personality traits. However, aspects of gaze behavior during facial emotion recognition among the latter are still unclear. This study therefore investigated the relations between eye movements and facial emotion recognition among nonclinical individuals with schizotypal personality traits.A total of 83 nonclinical participants completed the Schizotypal Personality Questionnaire (SPQ) and performed a facial emotion recognition task. Their gaze behavior was recorded by an eye-tracker. Self-report questionnaires measuring anxiety, depressive symptoms, and alexithymia were administered.At the behavioral level, correlation analyses showed that higher SPQ scores were associated with lower surprise recognition accuracy scores. Eye-tracking data revealed that higher SPQ scores were associated with shorter dwell time on relevant facial features during sadness recognition. Regression analyses revealed that the total SPQ score was the only significant predictor of eye movements during sadness recognition, and depressive symptoms were the only significant predictor of surprise recognition accuracy. Furthermore, dwell time predicted response times for sadness recognition in that shorter dwell time on relevant facial features was associated with longer response times.Schizotypal traits may be associated with decreased attentional engagement in relevant facial features during sadness recognition and impede participants' response times. Slower processing and altered gaze patterns during the processing of sad faces could lead to difficulties in everyday social situations in which information must be rapidly processed to enable the successful interpretation of other people's behavior.

Menstrual cycle changes in estradiol, stress reactivity, and emotion recognition

Previous research suggests that estradiol (E2) influences mood, but the mechanisms underlying these effects remain unclear. The current study measured affect and two proposed mechanisms – psychosocial stress reactivity and emotion recognition – in the early and late follicular menstrual phases, which differ in E2 levels. 29 reproductive-aged women completed the Positive and Negative Affect Schedule – Extended (PANAS-X), a facial emotion recognition task, and the Trier Social Stress Test (TSST) in the early (EF; low E2) and late (LF; high E2) follicular phases. Visual analogue scales measured subjective stress, insecurity, rejection, irritability, and contentment throughout the TSST. Salivary E2 was measured during each menstrual phase, and salivary cortisol was assessed at baseline and at 10 and 20 minutes after the TSST. Positive affect (p=.012) was significantly higher in the LF phase, and negative affect (p=.022) was significantly higher in the EF phase. Accuracy for recognizing anger (p=.026) was significantly higher in the EF phase but not for the other emotions assessed. Cortisol AUC with respect to increase was larger in the EF phase (p=.005). Additionally, the change in E2 from the EF to the LF phase was associated with a decline in fear recognition accuracy (r=-.41, p=.028) and a decrease in post-TSST irritability (r=-.41, p=.037). These findings suggest that menstrual cycle increases in E2 are associated with increased positive affect, decreased negative affect, more favorable cortisol responses to psychosocial stress, and decreased recognition of negative emotions.

Ostracism modulates children's recognition of emotional facial expressions.

Ostracism has been shown to induce considerable physiological, behavioral and cognitive changes in adults. Previous research demonstrated its effects on children's cognitive and behavioral abilities, but less is known about its impact on their capacity to recognize subtle variations in social cues. The present study aimed at investigating whether social manipulations of inclusion and ostracism modulate emotion recognition abilities in children, and whether this modulation varies across childhood. To do so, 5- and 10-year-old children participated in a computer-based ball tossing game called Cyberball during which they were either included or ostracized. Then, they completed a facial emotion recognition task in which they were required to identify neutral facial expressions, or varying levels of intensity of angry and fearful facial expressions. Results indicated lower misidentification rates for children who were previously ostracized as compared to children who were previously included, both at 5 and 10 years of age. Moreover, when looking at children's accuracy and sensitivity to facial expressions, 5-year-olds' decoding abilities were affected by the social manipulation, while no difference between included and ostracized participants was observed for 10-year-olds. In particular, included and ostracized 10-year-old children as well as ostracized 5-year-olds showed higher accuracy and sensitivity for expressions of fear as compared to anger, while no such difference was observed for included 5-year-olds. Overall, the current study presents evidence that Cyberball-induced inclusion and ostracism modulate children's recognition of emotional faces.

Free-viewing gaze patterns reveal a mood-congruency bias in MDD during an affective fMRI/eye-tracking task

Major depressive disorder (MDD) has been related to abnormal amygdala activity during emotional face processing. However, a recent large-scale study (n = 28,638) found no such correlation, which is probably due to the low precision of fMRI measurements. To address this issue, we used simultaneous fMRI and eye-tracking measurements during a commonly employed emotional face recognition task. Eye-tracking provide high-precision data, which can be used to enrich and potentially stabilize fMRI readouts. With the behavioral response, we additionally divided the active task period into a task-related and a free-viewing phase to explore the gaze patterns of MDD patients and healthy controls (HC) and compare their respective neural correlates. Our analysis showed that a mood-congruency attentional bias could be detected in MDD compared to healthy controls during the free-viewing phase but without parallel amygdala disruption. Moreover, the neural correlates of gaze patterns reflected more prefrontal fMRI activity in the free-viewing than the task-related phase. Taken together, spontaneous emotional processing in free viewing might lead to a more pronounced mood-congruency bias in MDD, which indicates that combined fMRI with eye-tracking measurement could be beneficial for our understanding of the underlying psychopathology of MDD in different emotional processing phases.Trial Registration: The BeCOME study is registered on ClinicalTrials (gov: NCT03984084) by the Max Planck Institute of Psychiatry in Munich, Germany.

Social Cognition in Opioid Use Disorder

Background: Opioid use disorder (OUD) is associated with significant functional impairment and neurocognitive dysfunction, but only a handful of studies have investigated social cognitive abilities in this condition. This study aimed to investigate facial emotion recognition accuracy/biases and two different aspects of theory of mind (ToM) (ToM-decoding vs ToM-reasoning) in people with recovered OUD. Methods: The participants included 32 people with recovered OUD who were on Buprenorphine + Naloxone (B/N) maintenance treatment and 32 healthy controls. In addition to neurocognitive tasks, both groups were assessed by a facial emotion recognition task, the faux pas recognition task, and the reading the mind from the eyes task. Results: In comparison to healthy controls, people on B/N maintenance treatment showed deficits in facial emotion recognition (d = 1.32) and both aspects of ToM (d = 0.87–1.21). In analyses of individual emotions, people on B/N maintenance treatment had decreased accuracy in recognition of anger and fear and had a bias to identify other emotions as sad. The duration of opioid use was robustly associated with difficulties in the recognition of anger. Conclusion: People in B/N maintenance treatment have significant difficulties in recognizing the emotions and mental states of others. Deficits in social cognition might be important for understanding the difficulties in interpersonal and social functioning in people with OUD.

Revealing concealed spontaneous facial micro-expression: Are we a step closer to unveil real-life behavioral expressions?

Facial micro-expression (ME) expression analysis has aroused interdisciplinary attention from the realms of computer vision and psychology due to its advantages of divulging nonverbal emotion via subtle involuntary facial muscle changes. However, developing and commercializing ME recognition relevant products has been hindered in current progress due to the scarcity of databases depicting real-world conditions. This paper aims to give a strong impetus to facilitating the advancement of ME recognition systems, particularly in in-the-wild applications. Succinctly, an efficient recognition system is introduced herein, which incorporates the core processes like 3D facial reconstruction, apex spotting, and emotion recognition tasks. Concretely, all faces are first rendered into 3D point cloud form for the brevity of face alignment along the video. Then, the optical flow-guided components are employed as the primary features to represent the motion details. Subsequently, a 3-Stream channel based on 3-Dimensional faces Network (3T3D-Net) with skip connection via multiplication is tailored to cope with the small-size input image. As a result, pleasing recognition performance is yielded via the suite of novel techniques devised, by producing a UAR of 69.44% and UF1 of 70.71%, when evaluated on the in-the-wild ME dataset, viz, MEVIEW. In addition, extensive studies and analyses are presented to verify the contribution of each component of the proposed pipeline. It is envisioned that the remarkable result attained by casting an invigorating perspective will shed light and pave a new prospect for analysis in ME applications.