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Simple Summary: Sheep often show signs of pain through their facial expressions, making these cues essential for monitoring their health and well-being. Quickly and accurately detecting these expressions is vital for effective pain management and preventing the spread of disease. However, existing detection methods can be slow, inaccurate, or unreliable, especially in challenging conditions like poor lighting or when sheep are partially hidden. To address these issues, we developed a new computer algorithm that automatically recognizes sheep facial expressions associated with pain. Our approach improves upon existing models by more precisely focusing on key facial features and filtering out irrelevant background information. In our tests, the algorithm showed significant improvements in accuracy, more reliably identifying normal and abnormal expressions compared to previous methods. This advancement allows farmers and veterinarians to monitor sheep health more effectively in real time, enabling quicker interventions when animals are in discomfort. Overall, our work provides a practical tool to enhance animal welfare by ensuring sheep receive timely and appropriate care. Sheep facial expressions are valuable indicators of their pain levels, playing a critical role in monitoring their health and welfare. In response to challenges such as missed detections, false positives, and low recognition accuracy in sheep facial expression recognition, this paper introduces an enhanced algorithm based on YOLOv8n, referred to as SimAM-MobileViTAttention-EfficiCIoU-AA2_SPPF-YOLOv8n (SMEA-YOLOv8n). Firstly, the proposed method integrates the parameter-free Similarity-Aware Attention Mechanism (SimAM) and MobileViTAttention modules into the CSP Bottleneck with 2 Convolutions(C2f) module of the neck network, aiming to enhance the model's feature representation and fusion capabilities in complex environments while mitigating the interference of irrelevant background features. Additionally, the EfficiCIoU loss function replaces the original Complete IoU(CIoU) loss function, thereby improving bounding box localization accuracy and accelerating model convergence. Furthermore, the Spatial Pyramid Pooling-Fast (SPPF) module in the backbone network is refined with the addition of two global average pooling layers, strengthening the extraction of sheep facial expression features and bolstering the model's core feature fusion capacity. Experimental results reveal that the proposed method achieves a mAP@0.5 of 92.5%, a Recall of 91%, a Precision of 86%, and an F1-score of 88.0%, reflecting improvements of 4.5%, 9.1%, 2.8%, and 6.0%, respectively, compared to the baseline model. Notably, the mAP@0.5 for normal and abnormal sheep facial expressions increased by 3.7% and 5.3%, respectively, demonstrating the method's effectiveness in enhancing recognition accuracy under complex environmental conditions. [ABSTRACT FROM AUTHOR]

The objective of the article is to recognize users' emotions by classifying facial electromyographic (EMG) signals. A biomedical signal amplifier, equipped with eight active electrodes positioned in accordance with the Facial Action Coding System, was used to record the EMG signals. These signals were registered during a procedure where users acted out various emotions: joy, sadness, surprise, disgust, anger, fear, and neutral. Recordings were made for 16 users. The mean power of the EMG signals formed the feature set. We utilized these features to train and evaluate various classifiers. In the subject-dependent model, the average classification accuracies were 96.3% for KNN, 94.9% for SVM with a linear kernel, 94.6% for SVM with a cubic kernel, and 93.8% for LDA. In the subject-independent model, the classification results varied depending on the tested user, ranging from 91.4% to 48.6% for the KNN classifier, with an average accuracy of 67.5%. The SVM with a cubic kernel performed slightly worse, achieving an average accuracy of 59.1%, followed by the SVM with a linear kernel at 53.9%, and the LDA classifier at 41.2%. Additionally, the study identified the most effective electrodes for distinguishing between pairs of emotions. [ABSTRACT FROM AUTHOR]

Abstract 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.

Sheep facial expressions are valuable indicators of their pain levels, playing a critical role in monitoring their health and welfare. In response to challenges such as missed detections, false positives, and low recognition accuracy in sheep facial expression recognition, this paper introduces an enhanced algorithm based on YOLOv8n, referred to as SimAM-MobileViTAttention-EfficiCIoU-AA2_SPPF-YOLOv8n (SMEA-YOLOv8n). Firstly, the proposed method integrates the parameter-free Similarity-Aware Attention Mechanism (SimAM) and MobileViTAttention modules into the CSP Bottleneck with 2 Convolutions(C2f) module of the neck network, aiming to enhance the model’s feature representation and fusion capabilities in complex environments while mitigating the interference of irrelevant background features. Additionally, the EfficiCIoU loss function replaces the original Complete IoU(CIoU) loss function, thereby improving bounding box localization accuracy and accelerating model convergence. Furthermore, the Spatial Pyramid Pooling-Fast (SPPF) module in the backbone network is refined with the addition of two global average pooling layers, strengthening the extraction of sheep facial expression features and bolstering the model’s core feature fusion capacity. Experimental results reveal that the proposed method achieves a mAP@0.5 of 92.5%, a Recall of 91%, a Precision of 86%, and an F1-score of 88.0%, reflecting improvements of 4.5%, 9.1%, 2.8%, and 6.0%, respectively, compared to the baseline model. Notably, the mAP@0.5 for normal and abnormal sheep facial expressions increased by 3.7% and 5.3%, respectively, demonstrating the method’s effectiveness in enhancing recognition accuracy under complex environmental conditions.

The aim of this study is designed an improved ResNet 50 network to achieve automatic classification model for pain expressions by elderly patients with hip fractures. This study built a dataset by combining the advantages of deep learning in image recognition, using a hybrid of the Multi-Task Cascaded Convolutional Neural Networks (MTCNN). Based on ResNet50 network framework utilized transfer learning to implement model function. This study performed the hyperparameters by Bayesian optimization in the learning process. This study calculated intraclass correlation between visual analog scale scores provided by clinicians independently and those provided by pain expression evaluation assistant(PEEA). The automatic pain expression recognition model in elderly patients with hip fractures, which constructed using the algorithm. The accuracy achieved 99.6% on the training set, 98.7% on the validation set, and 98.2% on the test set. The substantial kappa coefficient of 0.683 confirmed the efficacy of PEEA in clinic. This study demonstrates that the improved ResNet50 network can be used to construct an automatic pain expression recognition model for elderly patients with hip fractures, which has higher accuracy.

In order to achieve facial object detection and tracking in video, a method based on nonlinear sequence Monte Carlo filtering technology is proposed. The algorithm is simple, effective, and easy to operate, which can solve the problems of scale change and occlusion in the process of online learning tracking, so as to ensure the smooth implementation of learning effect evaluation. Experimental methods should be added to the article summary section. The results show that the algorithm in this study outperforms the basic KCF in terms of evaluation accuracy and success rate, as well as outperforms other tracker algorithms in benchmark, achieving scores of 0.837 and 0.705, respectively. In terms of overlapping accuracy, the reason why this study’s algorithm is higher than KCF is that this study determines the tracking status of the current target by calculating the primary side regulated (PSR) value when the target is obscured or lost, which does not make the tracking error to accumulate. The tracking algorithm in this study is not ranked first in the two attributes of motion blur and low resolution, but the rankings of all other nine attributes belong to the first. Compared with the KCF algorithm, the accuracy plots for the three attributes of scale change, occlusion, and leaving the field of view are improved by 10.26, 13.48, and 13.04%, respectively. Thus, it is proved that the method based on nonlinear sequence Monte Carlo filtering technology can achieve video facial object detection and tracking.

Parkinson's Disease (PD) is associated with motor and non-motor symptoms. Among the latter are deficits in matching, identification, and recognition of emotional facial expressions. On one hand, this deficit has been attributed to a dysfunction in emotion processing. Another explanation (which does not exclude the former) links this deficit with reduced facial expressiveness in these patients, which prevents them from properly understanding or embodying emotions. To disentangle the specific contribution of emotion comprehension and that of facial expression processing in PD's observed deficit with emotions we performed two experiments on non-emotional facial expressions. In Experiment 1, a group of PD patients and a group of Healthy Controls (HC) underwent a task of non-emotional expression recognition in faces of different identity and a task of identity recognition in faces with different expression. No differences were observed between the two groups in accuracies. In Experiment 2, PD patients and Healthy Controls underwent a task where they had to recognize the identity of faces encoded through a non-emotional facial expression, through a rigid head movement, or as neutral. Again, no group differences were observed. In none of the two experiments hypomimia scores had a specific effect on expression processing. We conclude that in PD patients the observed impairment with emotional expressions is likely due to a specific deficit for emotions to a greater extent than for facial expressivity processing.

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of the article is to recognize users’ emotions by classifying facial electromyographic (EMG) signals. A biomedical signal amplifier, equipped with eight active electrodes positioned in accordance with the Facial Action Coding System, was used to record the EMG signals. These signals were registered during a procedure where users acted out various emotions: joy, sadness, surprise, disgust, anger, fear, and neutral. Recordings were made for 16 users. The mean power of the EMG signals formed the feature set. We utilized these features to train and evaluate various classifiers. In the subject-dependent model, the average classification accuracies were 96.3% for KNN, 94.9% for SVM with a linear kernel, 94.6% for SVM with a cubic kernel, and 93.8% for LDA. In the subject-independent model, the classification results varied depending on the tested user, ranging from 91.4% to 48.6% for the KNN classifier, with an average accuracy of 67.5%. The SVM with a cubic kernel performed slightly worse, achieving an average accuracy of 59.1%, followed by the SVM with a linear kernel at 53.9%, and the LDA classifier at 41.2%. Additionally, the study identified the most effective electrodes for distinguishing between pairs of emotions.

In traditional learning situations, teachers mainly evaluate students’ behavioral changes, emotional changes, and interactions to ensure teaching quality. The Random Forest algorithm is employed in this paper to determine the characteristics of students’ body postures and observe their behavioral changes. The geometric analysis method is used to recognize students’ head posture, and the image test classification module is used to capture students’ facial expressions. Analyze the teacher-student learning interactions in the classroom by examining the student’s question-answering records from the interactive cloud platform. A multimodal information fusion model was constructed to combine students’ multimodal performance data to complete the evaluation of the preschool hygiene course. The results of the case study of five students showed that the fusion results of the ratings of students 1-5 under the multimodal information fusion model of the course evaluation were 0.56, 0.888, 0.083, 0.452, and 0.957, respectively. The results of the fusion of the ratings of students 2 and 5 were very serious in learning the course, and the percentage of their time spent in attentive behavior was 0.452. Student 2 and Student 5 studied the course very seriously, spent 90% and 89% of their time engaged in attentive behaviors, respectively, and maintained an emotion of interest for 66% and 70% of the time, respectively. Furthermore, Student 2 answered all the questions in the interactive session, while Student 5 answered all the questions correctly. The median integration rating of the 40 participating students was 0.706, and the majority of the students rated the integration of the course in the middle to high range, which means that the content and format of this pre-school hygiene course were excellent and stimulating for the students.

A computer vision model is constructed to recognize the expressions and movements of musical performers so as to analyze the changes in the forms and techniques of musical performances under the constructivist theory. The model needs to perform pre-processing, such as grayscale conversion, thresholding, background subtraction, noise reduction, and so on, on the related video before recognizing expressions and actions. The performer’s movements are recognized based on TC-HGCN, while the expressions are recognized based on CSACNN. The average accuracy of TC-HGCN in recognizing dance movements in musicals reaches 87.95%. The facial expression recognition method based on CSACNN can overcome the shortcomings of commonly used expression recognition models. The use of computer vision modeling analyzes the constructivist theory on the form and technique of the next musical performance, which enhances the performance effect and has more infectious and artistic charm.

It takes ten years to grow trees and a hundred years to nurture people. Education, as the most indispensable link in establishing morality and cultivating people, has a very important role in modern society. The study explores the application of ancient Chinese educational concepts to modern educational models, proposes a modernized teaching model based on ancient educational concepts, and uses the YOLO model to construct a classroom teaching expression and action recognition model under ancient educational concepts. The model is used in the actual classroom to analyze the expressions and behaviors of students in the classroom to explore their classroom concentration, which is used to analyze the quality of classroom teaching. By comparing the differences and changes in the performance of the experimental group and the control group, the effect of the modernized teaching model based on the ancient education concept on actual teaching is explored. Through the model test, the classroom students’ concentration score is 72.71. The pre-test P-value of the experimental group and the control group is 0.846, and the post-test P-value is 0.000, which indicates that there is a significant difference in the performance of the two groups after the experiment. The increase in the performance of the students in the experimental group was 31.390 points, while in the control group, it was 5.175 points. The P-values of the students in the experimental group on the five dimensions of math level are less than 0.05, and the control group is greater than 0.05, which indicates that the modernized teaching model based on the ancient concept of education has a significant positive effect on improving the performance of the students.

In the education system, teachers and students as the main body of the classroom; their emotional state in the classroom school is an important indicator of the effectiveness of the classroom. This study first explores biometric recognition, based on the needs of the classroom curriculum and the classroom monitoring as a sensor, to propose a multimodal biometric fusion detection method based on the fusion of face and gait recognition. The PCA algorithm is used to optimize the face recognition as well as the occlusion situation in the classroom to improve gait recognition, and then the face and gait are fused based on the decision layer to achieve the detection and recognition of the identity situation of teachers and students. On this basis, an expression recognition model is established using the attention mechanism, and an emotion analysis system is designed for the classroom curriculum. According to the empirical evidence of multimodal biometric fusion sentiment analysis, the mAP accuracy of this paper’s fusion method is 100% in Euclidean distance, and the accuracy is higher than 99% in cosine distance, which is obviously better than other methods, and the accuracy of this paper’s fusion recognition is above 95% under any condition limitations. At the same time, the correct rate of recognition of emotions such as listening, appreciation, resistance, doubt, and inattention are all higher than 85%, and the five indexes of average absolute error, Pearson correlation coefficient, Accuarcy5, Accuarcy2, and F12 score of this paper’s sentiment analysis have achieved the best results comparing with other sentiment analysis models, which proves the generalization and validity of this paper’s sentiment analysis.

With the development of the times and the continuous innovation of modern education technology, the multimodal theory is developing deeply in the field of education. The first step in this study is to use the optimization principle to select modes that meet the needs of English translation teaching and then develop a multimodal analysis framework for external communication that incorporates multimedia communication methods. Then, we constructed an emotion recognition model for English translation teaching by using speech recognition and expression recognition combined with convolutional neural networks for the students’ classroom situation and attitude and carried out the empirical analysis. The recognition accuracy of the emotion recognition system constructed in this paper is 86.4%. In the multimodal analysis of English translation teaching, the teacher who uses the linguistic modality type for the most number of times 387 times, and the teacher who uses the mixed modality for the most number of times 271 times, the students have the best classroom status. It has been stable at about 5 points, which is a “more positive” state. The students in the class had the highest level of motivation. Through the analysis, it is found that multimodal teaching can effectively improve the students’ status in class and keep their attention, which verifies the appropriateness of the external communication path combined with multimedia communication in multimodal English translation teaching. It provides a practical path for the development of English translation teaching.

With the informatization of education, the traditional English teaching evaluation method is increasingly unable to meet the needs of cultivating talents in colleges and universities. In this paper, we constructed an informatized English teaching evaluation system using two modules: students’ classroom learning status evaluation and teachers’ teaching quality evaluation. Using informationized face detection and expression recognition technology, the evaluation axis of students’ classroom learning status is established as “students’ head-up rate - students’ expression - students’ concentration - classroom learning status”. The fuzzy comprehensive judgment model based on a genetic algorithm was established by constructing a judgment matrix and calculating the weights of teaching quality evaluation indexes using a hierarchical analysis method. The experimental and control groups analyzed the English teaching evaluation system’s application. Regarding English achievement, the post-experimental scores of the students in the experimental class were 8.57 points higher than those of the control class, with a highly significant difference (P

This study examined whether three subtypes of anxiety (trait anxiety, state anxiety, and social anxiety) have different effects on recognition of facial expressions. One hundred and thirty-eight participants matched facial expressions of three intensity levels (20 %, 40 %, 100 %) with one of the six emotion labels (“happy”, “sad”, “fear”, “angry”, “disgust”, and “surprise”). While using a conventional method of analysis we were able to replicate some significant correlations between each anxiety type and recognition performance found in the literature. However, when we used partial correlation to isolate the effect of each anxiety type, most of these correlations were no longer significant, apart from the negative correlations between Beck Anxiety Inventory and reaction time to fearful faces displayed at 40 % intensity level, and the correlations between anxiety and categorisation errors. Specifically, social anxiety was positively correlated with misidentifying a happy face as a disgust face at 40 % intensity level, and state anxiety negatively correlated with misidentifying a happy face as a sad face at 20 % intensity level. However, these partial correlation analyses became non-significant after p value adjustment for multiple comparisons. Our eye tracking data also showed that state anxiety may be associated with reduced fixations on the eye regions of low-intensity sad or fearful faces. These analyses cast doubts on some effects reported in the previous studies because they are likely to reflect a mixture of influences from highly correlated anxiety subtypes.

Facial expression recognition is an important research direction of emotion computing and has broad application prospects in human-computer interaction. However, noise such as illumination and occlusion in natural environment brings many challenges to facial expression recognition. In order to solve the problems such as low recognition rate of facial expression in natural environment, unable to highlight the characteristics of facial expression in global facial research, and misclassification caused by the similarity between negative expressions. In this paper, a multi-region coordinate attentional residual expression recognition model (MrCAR) is proposed. The model is mainly composed of the following three parts: 1) multi-region input: MTCNN is used for face detection and alignment processing, and the eyes and mouth parts are further cropped to obtain multi-region pictures. Through multi-region input, local details and global features are more easily obtained, which reduces the influence of complex environmental noise and highlights the facial features. 2) Feature extraction module: On the basis of residual element, CA-Net and multi-scale convolution were added to obtain coordinate residual attention module, through which the model’s ability to distinguish subtle changes of expression and the utilization rate of key features were improved; 3) Classifier: Arcface Loss is used to enhance intra-class tightness and inter-class difference at the same time, thus reducing the wrong classification of negative expressions by the model. Finally, the accuracy rates of CK+, JAFFE, FER2013 and RAF-DB were 98.78%, 99.09%, 74.50% and 88.26%, respectively. The experimental results show that compared with many advanced models, the MrCAR model in this paper is more competent for the task of expression classification.

Article Highlights 1. In this paper, we describe a way to make a new face expression dataset by putting together open source and self-collected datasets. 2. This allows us to create the largest face expression dataset in the industry, which is better in quality and has more data. 3. When we use this new dataset to train a face expression recognition model, it performs the best in the current industry.

Face images in the logarithmic space can be considered as a sum of the texture component and lighting map component according to Lambert Reflection. However, it is still not easy to separate these two parts, because face contour boundaries and lighting change boundaries are difficult to distinguish. In order to enhance the separation quality of these to parts, this paper proposes an illumination standardization algorithm based on extreme L0 Gaussian difference regularization constraints, assuming that illumination is massively spread all over the image but illumination change boundaries are simple, regular, and sparse enough. The proposed algorithm uses an iterative L0 Gaussian difference smoothing method, which achieves a more accurate lighting map estimation by reserving the fewest boundaries. Thus, the texture component of the original image can be restored better by simply subtracting the lighting map estimated. The experiments in this paper are organized with two steps: the first step is to observe the quality of the original texture restoration, and the second step is to test the effectiveness of our algorithm for complex face classification tasks. We choose the facial expression classification in this step. The first step experimental results show that our proposed algorithm can effectively recover face image details from extremely dark or light regions. In the second step experiment, we use a CNN classifier to test the emotion classification accuracy, making a comparison of the proposed illumination removal algorithm and the state-of-the-art illumination removal algorithm as face image preprocessing methods. The experimental results show that our algorithm works best for facial expression classification at about 5 to 7 percent accuracy higher than other algorithms. Therefore, our algorithm is proven to provide effective lighting processing technical support for the complex face classification problems which require a high degree of preservation of facial texture. The contribution of this paper is, first, that this paper proposes an enhanced TV model with an L0 boundary constraint for illumination estimation. Second, the boundary response is formulated with the Gaussian difference, which strongly responds to illumination boundaries. Third, this paper emphasizes the necessity of reserving details for preprocessing face images. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Large-scale facial expression datasets are primarily composed of real-world facial expressions. Expression occlusion and large-angle faces are two important problems affecting the accuracy of expression recognition. Moreover, because facial expression data in natural scenes commonly follow a long-tailed distribution, trained models tend to recognize the majority classes while recognizing the minority classes with low accuracies. To improve the robustness and accuracy of expression recognition networks in an uncontrolled environment, this paper proposes an efficient network structure based on an attention mechanism that fuses global and local features (AM-FGL). We use a channel spatial model and local feature convolutional neural networks to perceive the global and local features of the human face, respectively. Because the distribution of real-world scene field expression datasets commonly follows a long-tail distribution, where neutral and happy expressions account for the tail expressions, a trained model exhibits low recognition accuracy for tail expressions such as fear and disgust. CutMix is a novel data enhancement method proposed in other fields; thus, based on the CutMix concept, a simple and effective data-balancing method is proposed (BC-EDB). The key idea is to paste key pixels (around eyes, mouths, and noses), which reduces the influence of overfitting. Our proposed method is more focused on the recognition of tail expression, occluded expression, and large-angle faces, and we achieved the most advanced results in occlusion-RAF-DB, 30 ∘ pose-RAF-DB, and 45 ∘ pose-RAF-DB with accuracies of 86.96%, 89.74%, and 88.53%. [ABSTRACT FROM AUTHOR]

Because of the development of metaverse, robotics, emotional computing has become a very active research topic. And the most important part of affective computing is facial expression recognition, The existing deep learning-based face expression recognition methods are trained on small face expression datasets, and the current datasets have the problems of low quantity, low quality and category imbalance, and the models are prone to overfitting when trained on these small datasets. Compared with other methods using small datasets, this paper proposes a method to build a large face expression dataset by collecting data from the Internet. It also uses both downsampling and data augmentation to solve the category imbalance problem. Meanwhile, this paper experiments the effect of different network structures on face expression recognition based on this brand new dataset. Compared to approaches using small datasets such as raf-db, fer2013, and relatively large but category-unbalanced datasets such as ExpW and AffectNet, the densenet-based model proposed in this paper, trained on this dataset, achieved 97.5% accuracy on the CK+ benchmark, achieve the state-of-the-art performance, which confirms the effectiveness of our proposed approach. Article Highlights: In this paper, we describe a way to make a new face expression dataset by putting together open source and self-collected datasets. This allows us to create the largest face expression dataset in the industry, which is better in quality and has more data. When we use this new dataset to train a face expression recognition model, it performs the best in the current industry. [ABSTRACT FROM AUTHOR]

Expression recognition is a very important direction for computers to understand human emotions and human-computer interaction. However, for 3D data such as video sequences, the complex structure of traditional convolutional neural networks, which stretch the input 3D data into vectors, not only leads to a dimensional explosion, but also fails to retain structural information in 3D space, simultaneously leading to an increase in computational cost and a lower accuracy rate of expression recognition. This paper proposes a video sequence face expression recognition method based on Squeeze-and-Excitation and 3DPCA Network (SE-3DPCANet). The introduction of a 3DPCA algorithm in the convolution layer directly constructs tensor convolution kernels to extract the dynamic expression features of video sequences from the spatial and temporal dimensions, without weighting the convolution kernels of adjacent frames by shared weights. Squeeze-and-Excitation Network is introduced in the feature encoding layer, to automatically learn the weights of local channel features in the tensor features, thus increasing the representation capability of the model and further improving recognition accuracy. The proposed method is validated on three video face expression datasets. Comparisons were made with other common expression recognition methods, achieving higher recognition rates while significantly reducing the time required for training. [ABSTRACT FROM AUTHOR]

Introduction: To explore a quick and non-invasive way to measure individual psychological states, this study developed interview-based scales, and multi-modal information was collected from 172 participants. Methods: We developed the Interview Psychological Symptom Inventory (IPSI) which eventually retained 53 items with nine main factors. All of them performed well in terms of reliability and validity. We used optimized convolutional neural networks and original detection algorithms for the recognition of individual facial expressions and physical activity based on Russell's circumplex model and the five factor model. Results: We found that there was a significant correlation between the developed scale and the participants' scores on each factor in the Symptom Checklist-90 (SCL-90) and Big Five Inventory (BFI-2) [r = (−0.257, 0.632), p < 0.01]. Among the multi-modal data, the arousal of facial expressions was significantly correlated with the interval of validity (p < 0.01), valence was significantly correlated with IPSI and SCL-90, and physical activity was significantly correlated with gender, age, and factors of the scales. Discussion: Our research demonstrates that mental health can be monitored and assessed remotely by collecting and analyzing multimodal data from individuals captured by digital tools. [ABSTRACT FROM AUTHOR]

It is generally accepted that the use of the most informative areas of the input image significantly optimizes visual processing. Several authors agree that, the areas of spatial heterogeneity are the most interesting for the visual system and the degree of difference between those areas and their surroundings determine the saliency. The purpose of our study was to test the hy-pothesis that the most informative are the areas of the image of largest increase in total luminance contrast, and information from these areas is used in the process of categorization facial expressions. Using our own program that was developed to imitate the work of second-order visual mechanisms, we created stimuli from the initial photographic images of faces with 6 basic emotions and a neutral expression. These images consisted only of areas of highest increase in total luminance contrast. Initially, we determined the spatial frequency ranges in which the selected areas contain the most useful information for the recognition of each of the expressions. We then compared the expressions recognition accuracy in images of real faces and those synthe-sized from the areas of highest contrast increase. The obtained results indicate that the recognition of expressions in synthe-sized images is somewhat worse than in real ones (73% versus 83%). At the same time, the partial loss of information that oc-curs due to the replacing real and synthesized images does not disrupt the overall logic of the recognition. Possible ways to make up for the missing information in the synthesized images are suggested.

Nowadays, the facial expression recognition (FER) task has transitioned from a laboratory-controlled scenario to in-the-wild conditions. However, recognizing facial expressions in the wild is challenging due to factors such as variant backgrounds, low-quality facial images, and the subjectiveness of annotators. Therefore, deep neural networks have increasingly been leveraged to learn discriminative representations for FER. In this work, we propose the Multi-cues Fusion Net (MCF-Net), a novel deep learning model with a two-stream structure for FER. Our model first proposes a two-stream coding network to extract face and scene representations. Then, an adaptive fusion module is employed to fuse the two different representations for final recognition. In the face coding stream, a Sparse Mask Attention Learning (SMAL) module is utilized to adaptively generate the corresponding sparse face mask according to the input image. Meanwhile, we employ a Multi-scale Attention (MSA) module for extracting fine-grained feature subsets, which can obtain richer multi-scale interaction information. In the scene coding stream, a Relational Attention (RA) module is applied to construct the hidden relationship between the face and contextual features of non-facial regions by capturing the pairwise similarity. In order to verify the effectiveness and accuracy of our model, a large number of experiments are carried out on two public large-scale static facial expression image datasets, CAER-S and NCAER-S. By comparing the performance of our MCF-Net with other methods, the proposed model achieves superior results on two in-the-wild FER benchmarks: CAER-S with an accuracy of 81.82% and NCAER-S with an accuracy of 45.59%. [ABSTRACT FROM AUTHOR]

It is generally accepted that the use of the most informative areas of the input image significantly optimizes visual processing. Several authors agree that, the areas of spatial heterogeneity are the most interesting for the visual system and the degree of difference between those areas and their surroundings determine the saliency. The purpose of our study was to test the hy-pothesis that the most informative are the areas of the image of largest increase in total luminance contrast, and information from these areas is used in the process of categorization facial expressions. Using our own program that was developed to imitate the work of second-order visual mechanisms, we created stimuli from the initial photographic images of faces with 6 basic emotions and a neutral expression. These images consisted only of areas of highest increase in total luminance contrast. Initially, we determined the spatial frequency ranges in which the selected areas contain the most useful information for the recognition of each of the expressions. We then compared the expressions recognition accuracy in images of real faces and those synthe-sized from the areas of highest contrast increase. The obtained results indicate that the recognition of expressions in synthe-sized images is somewhat worse than in real ones (73% versus 83%). At the same time, the partial loss of information that oc-curs due to the replacing real and synthesized images does not disrupt the overall logic of the recognition. Possible ways to make up for the missing information in the synthesized images are suggested. [ABSTRACT FROM AUTHOR]

In view of Lhe problems of low raLe of infa nL expression recognilion and Lhe insufficienl exLracLion of complex features, an infant expression recognition algorithm based on MobileNetV2 and Ll3P dual - channel feature fusion is proposed. The first channel uses the improved MobileNetV2 network to quickly and accurately extract the global features of facial expressions. The second channel divides the original input image into blocks, and uses image information entropy to construct weights, and extracts block - weighted LBP histogram features to highlight the regions wiLh rich expression informaLion. The ouLpuL vecLor of Lhe dual - channel model is fused Lo improve Lhe feaLure expression abiliLy, and Lhe supporL vecLor machine is used Lo replace Lhe SofLmax layer for expression classificalion. Experiments show that the use of fusion features has a better classification effect than a single feature, and in the self - built infant expression data set, the accuracy of expression recognition can reach 85. 71 % . [ABSTRACT FROM AUTHOR]

Face images in the logarithmic space can be considered as a sum of the texture component and lighting map component according to Lambert Reflection. However, it is still not easy to separate these two parts, because face contour boundaries and lighting change boundaries are difficult to distinguish. In order to enhance the separation quality of these to parts, this paper proposes an illumination standardization algorithm based on extreme L0 Gaussian difference regularization constraints, assuming that illumination is massively spread all over the image but illumination change boundaries are simple, regular, and sparse enough. The proposed algorithm uses an iterative L0 Gaussian difference smoothing method, which achieves a more accurate lighting map estimation by reserving the fewest boundaries. Thus, the texture component of the original image can be restored better by simply subtracting the lighting map estimated. The experiments in this paper are organized with two steps: the first step is to observe the quality of the original texture restoration, and the second step is to test the effectiveness of our algorithm for complex face classification tasks. We choose the facial expression classification in this step. The first step experimental results show that our proposed algorithm can effectively recover face image details from extremely dark or light regions. In the second step experiment, we use a CNN classifier to test the emotion classification accuracy, making a comparison of the proposed illumination removal algorithm and the state-of-the-art illumination removal algorithm as face image preprocessing methods. The experimental results show that our algorithm works best for facial expression classification at about 5 to 7 percent accuracy higher than other algorithms. Therefore, our algorithm is proven to provide effective lighting processing technical support for the complex face classification problems which require a high degree of preservation of facial texture. The contribution of this paper is, first, that this paper proposes an enhanced TV model with an L0 boundary constraint for illumination estimation. Second, the boundary response is formulated with the Gaussian difference, which strongly responds to illumination boundaries. Third, this paper emphasizes the necessity of reserving details for preprocessing face images.

In 21st-century society, with the rapid development of information technology, the scientific and technological strength of all walks of life is increasing, and the field of education has also begun to introduce high and new technologies gradually. Affected by the epidemic, online teaching has been implemented all over the country, forming an education model of "dual integration" of online and offline teaching. However, the disadvantages of online teaching are also very obvious; that is, teachers cannot understand the students' listening status in real-time. Therefore, our study adopts automatic face detection and expression recognition based on a deep learning framework and other related technologies to solve this problem, and it designs an analysis system of students' class concentration based on expression recognition. The students' class concentration analysis system can help teachers detect students' class concentration and improve the efficiency of class evaluation. In this system, OpenCV is used to call the camera to collect the students' listening status in real-time, and the MTCNN algorithm is used to detect the face of the video to frame the location of the student's face image. Finally, the obtained face image is used for real-time expression recognition by using the VGG16 network added with ECANet, and the students' emotions in class are obtained. The experimental results show that the method in our study can more accurately identify students' emotions in class and carry out a teaching effect evaluation, which has certain application value in intelligent education fields, such as the smart classroom and distance learning. For example, a teaching evaluation module can be added to the teaching software, and teachers can know the listening emotions of each student in class while lecturing. [ABSTRACT FROM AUTHOR]

Expression recognition in the wild is easily distorted by nonfrontal and asymmetry faces. In nonfrontal faces, some areas are compressed and distorted. Even after frontalization, these compressed areas may still be blurred and distort expression recognition. Additionally, asymmetrical expressions are common on half or local face areas and produce incorrect expression features. Therefore, this paper presents a half-face frontalization and pyramid Fourier frequency conversion method. Despite the location, range and intensity of incorrect expressions in nonfrontal faces being unknown, according to discrete Fourier transform, it can be proven that the frequency band of the correct expression is much larger than that of incorrect expression on the same face. This can be taken advantage of by pyramid frequency conversion, which is designed based on Fourier frequency conversion. It can adjust incorrect expression frequency in multiscales to take them out off the band-pass of the convolution operations of deep learning and be eliminated completely, whereas correct expression information is reserved. Thus, expressions can be recognized effectively.

A facial expression recognition (FER) algorithm is built on the advanced convolutional neural network (CNN) to improve the current FER algorithms' recognition rate. The advanced CNN model (the ExpressionNet model), containing two continuous convolutional (Conv) layers and one maximum cache layer, is obtained through the AlexNet CNN. The proposed algorithm is compared with the SingleNet model, CNN with three Conv layers, and the AlexNet model through simulation experiments. The experiment results show that the ExpressionNet model takes the longest training time and test time, followed by the AlexNet and the SingleNet. In terms of recognition rate, ExpressionNet (77%) is superior to AlexNet (72.5%) and SingleNet (69.5%); however, its convergence rate is slightly slower than the other two models. The ExpressionNet model has only one layer more than that of the AlexNet model. Therefore, the advanced CNN-based FER algorithm is of great significance to theoretical research and practical application of the FER technology. [ABSTRACT FROM AUTHOR]

Large-scale facial expression datasets are primarily composed of real-world facial expressions. Expression occlusion and large-angle faces are two important problems affecting the accuracy of expression recognition. Moreover, because facial expression data in natural scenes commonly follow a long-tailed distribution, trained models tend to recognize the majority classes while recognizing the minority classes with low accuracies. To improve the robustness and accuracy of expression recognition networks in an uncontrolled environment, this paper proposes an efficient network structure based on an attention mechanism that fuses global and local features (AM-FGL). We use a channel spatial model and local feature convolutional neural networks to perceive the global and local features of the human face, respectively. Because the distribution of real-world scene field expression datasets commonly follows a long-tail distribution, where neutral and happy expressions account for the tail expressions, a trained model exhibits low recognition accuracy for tail expressions such as fear and disgust. CutMix is a novel data enhancement method proposed in other fields; thus, based on the CutMix concept, a simple and effective data-balancing method is proposed (BC-EDB). The key idea is to paste key pixels (around eyes, mouths, and noses), which reduces the influence of overfitting. Our proposed method is more focused on the recognition of tail expression, occluded expression, and large-angle faces, and we achieved the most advanced results in occlusion-RAF-DB, 30∘ pose-RAF-DB, and 45∘ pose-RAF-DB with accuracies of 86.96%, 89.74%, and 88.53%.

Facial expressions are a form of non-verbal communication, they appear as changes on the surface of the facial skin according to one's inner emotional states, aims, or social communications. Classification of these expressions is a normal process for humans, but it is a challenging task for machines. Lately, interest in facial expression recognition has grown, and many systems have been developed to classify expressions from facial images. Any expression recognition system is comprised of three steps. The first one is face acquisition, then feature extraction, and finally classification. The classification accuracy depends primarily on the feature extraction step. Therefore, in this research we study many texture feature extraction descriptors and compare their results under the same preprocessing circumstances; moreover, we propose two improvements for one of these descriptors, which give better results than the original one. We validate the results on two commonly used databases for expression recognition using Matlab programming language, wishing all of that to be an interesting point for researchers in this field.

Expression recognition is a very important direction for computers to understand human emotions and human-computer interaction. However, for 3D data such as video sequences, the complex structure of traditional convolutional neural networks, which stretch the input 3D data into vectors, not only leads to a dimensional explosion, but also fails to retain structural information in 3D space, simultaneously leading to an increase in computational cost and a lower accuracy rate of expression recognition. This paper proposes a video sequence face expression recognition method based on Squeeze-and-Excitation and 3DPCA Network (SE-3DPCANet). The introduction of a 3DPCA algorithm in the convolution layer directly constructs tensor convolution kernels to extract the dynamic expression features of video sequences from the spatial and temporal dimensions, without weighting the convolution kernels of adjacent frames by shared weights. Squeeze-and-Excitation Network is introduced in the feature encoding layer, to automatically learn the weights of local channel features in the tensor features, thus increasing the representation capability of the model and further improving recognition accuracy. The proposed method is validated on three video face expression datasets. Comparisons were made with other common expression recognition methods, achieving higher recognition rates while significantly reducing the time required for training.

Background: Alcohol and cannabis are commonly used by adolescents in the United States. Both alcohol use disorder (AUD) and cannabis use disorder (CUD) have been associated with reduced emotion expression recognition ability. However, this work has primarily occurred in adults and has not considered neuro-cognitive risk factors associated with conduct problems that commonly co-occur with, and precede, substance use. Yet, conduct problems are also associated with reduced emotion expression recognition ability. The current study investigated the extent of negative association between AUD and CUD symptom severity and expression recognition ability over and above any association of expression recognition ability with conduct problems [conduct disorder (CD) diagnostic status].Methods: In this study, 152 youths aged 12.5–18 years (56 female; 60 diagnosed with CD) completed a rapid presentation morphed intensity facial expression task to investigate the association between relative severity of AUD/CUD and expression recognition ability.Results: Cannabis use disorder identification test (CUDIT) scores were negatively associated with recognition accuracy for higher intensity (particularly sad and fearful) expressions while CD diagnostic status was independently negatively associated with recognition of sad expressions. Alcohol use disorder identification test (AUDIT) scores were not significantly associated with expression recognition ability.Conclusions: These data indicate that relative severity of CUD and CD diagnostic status are statistically independently associated with reduced expression recognition ability. On the basis of these data, we speculate that increased cannabis use during adolescence may exacerbate a neuro-cognitive risk factor for the emergence of aggression and antisocial behavior.

Background: Alcohol and cannabis are commonly used by adolescents in the United States. Both alcohol use disorder (AUD) and cannabis use disorder (CUD) have been associated with reduced emotion expression recognition ability. However, this work has primarily occurred in adults and has not considered neuro-cognitive risk factors associated with conduct problems that commonly co-occur with, and precede, substance use. Yet, conduct problems are also associated with reduced emotion expression recognition ability. The current study investigated the extent of negative association between AUD and CUD symptom severity and expression recognition ability over and above any association of expression recognition ability with conduct problems [conduct disorder (CD) diagnostic status]. Methods: In this study, 152 youths aged 12.5–18 years (56 female; 60 diagnosed with CD) completed a rapid presentation morphed intensity facial expression task to investigate the association between relative severity of AUD/CUD and expression recognition ability. Results: Cannabis use disorder identification test (CUDIT) scores were negatively associated with recognition accuracy for higher intensity (particularly sad and fearful) expressions while CD diagnostic status was independently negatively associated with recognition of sad expressions. Alcohol use disorder identification test (AUDIT) scores were not significantly associated with expression recognition ability. Conclusions: These data indicate that relative severity of CUD and CD diagnostic status are statistically independently associated with reduced expression recognition ability. On the basis of these data, we speculate that increased cannabis use during adolescence may exacerbate a neuro-cognitive risk factor for the emergence of aggression and antisocial behavior. [ABSTRACT FROM AUTHOR]

The evaluations of traditional teaching quality are mainly subjective, and there is a lack of fine-grained objective data to support the evaluation of teaching states in the classroom. In this paper, an intensity-based facial expression dataset is proposed and named EIDB-13, which contains 13 kinds and 10393 facial images collected from thousands of individuals and existing facial expression datasets. Convolutional neural network (CNN) and attention mechanism are combined to recognize facial expressions. Migration learning is used to solve over-fitting problem in the process of training deep network based on the small sample dataset. InceptionResNetV2 is employed as migration network. Furthermore, an InceptionResNetV2+CBAM network proposed extract similar feature information among facial expressions and it outperforms the network without attention mechanisms. Experiments show a classification accuracy rate of 78% on the intensity-based facial expression dataset EIDB-13 and of 88% on the public macro expression dataset RAF-DB. Combining facial expression recognition technology into teaching is a key foundation to study teaching quality on the intensity of teacher's expression.

Nowadays, the facial expression recognition (FER) task has transitioned from a laboratory-controlled scenario to in-the-wild conditions. However, recognizing facial expressions in the wild is challenging due to factors such as variant backgrounds, low-quality facial images, and the subjectiveness of annotators. Therefore, deep neural networks have increasingly been leveraged to learn discriminative representations for FER. In this work, we propose the Multi-cues Fusion Net (MCF-Net), a novel deep learning model with a two-stream structure for FER. Our model first proposes a two-stream coding network to extract face and scene representations. Then, an adaptive fusion module is employed to fuse the two different representations for final recognition. In the face coding stream, a Sparse Mask Attention Learning (SMAL) module is utilized to adaptively generate the corresponding sparse face mask according to the input image. Meanwhile, we employ a Multi-scale Attention (MSA) module for extracting fine-grained feature subsets, which can obtain richer multi-scale interaction information. In the scene coding stream, a Relational Attention (RA) module is applied to construct the hidden relationship between the face and contextual features of non-facial regions by capturing the pairwise similarity. In order to verify the effectiveness and accuracy of our model, a large number of experiments are carried out on two public large-scale static facial expression image datasets, CAER-S and NCAER-S. By comparing the performance of our MCF-Net with other methods, the proposed model achieves superior results on two in-the-wild FER benchmarks: CAER-S with an accuracy of 81.82% and NCAER-S with an accuracy of 45.59%.

Due to the importance of facial expressions in human–machine interaction, a number of handcrafted features and deep neural networks have been developed for facial expression recognition. While a few studies have shown the similarity between the handcrafted features and the features learned by deep network, a new feature loss is proposed to use feature bias constraint of handcrafted and deep features to guide the deep feature learning during the early training of network. The feature maps learned with and without the proposed feature loss for a toy network suggest that our approach can fully explore the complementarity between handcrafted features and deep features. Based on the feature loss, a general framework for embedding the traditional feature information into deep network training was developed and tested using the FER2013, CK+, Oulu-CASIA, and MMI datasets. Moreover, adaptive loss weighting strategies are proposed to balance the influence of different losses for different expression databases. The experimental results show that the proposed feature loss with adaptive weighting achieves much better accuracy than the original handcrafted feature and the network trained without using our feature loss. Meanwhile, the feature loss with adaptive weighting can provide complementary information to compensate for the deficiency of a single feature. [ABSTRACT FROM AUTHOR]

In 21st-century society, with the rapid development of information technology, the scientific and technological strength of all walks of life is increasing, and the field of education has also begun to introduce high and new technologies gradually. Affected by the epidemic, online teaching has been implemented all over the country, forming an education model of “dual integration” of online and offline teaching. However, the disadvantages of online teaching are also very obvious; that is, teachers cannot understand the students’ listening status in real-time. Therefore, our study adopts automatic face detection and expression recognition based on a deep learning framework and other related technologies to solve this problem, and it designs an analysis system of students’ class concentration based on expression recognition. The students’ class concentration analysis system can help teachers detect students’ class concentration and improve the efficiency of class evaluation. In this system, OpenCV is used to call the camera to collect the students’ listening status in real-time, and the MTCNN algorithm is used to detect the face of the video to frame the location of the student’s face image. Finally, the obtained face image is used for real-time expression recognition by using the VGG16 network added with ECANet, and the students’ emotions in class are obtained. The experimental results show that the method in our study can more accurately identify students’ emotions in class and carry out a teaching effect evaluation, which has certain application value in intelligent education fields, such as the smart classroom and distance learning. For example, a teaching evaluation module can be added to the teaching software, and teachers can know the listening emotions of each student in class while lecturing.

The aim of this study is designed an improved ResNet 50 network to achieve automatic classification model for pain expressions by elderly patients with hip fractures. This study built a dataset by combining the advantages of deep learning in image recognition, using a hybrid of the Multi-Task Cascaded Convolutional Neural Networks (MTCNN). Based on ResNet50 network framework utilized transfer learning to implement model function. This study performed the hyperparameters by Bayesian optimization in the learning process. This study calculated intraclass correlation between visual analog scale scores provided by clinicians independently and those provided by pain expression evaluation assistant(PEEA). The automatic pain expression recognition model in elderly patients with hip fractures, which constructed using the algorithm. The accuracy achieved 99.6% on the training set, 98.7% on the validation set, and 98.2% on the test set. The substantial kappa coefficient of 0.683 confirmed the efficacy of PEEA in clinic. This study demonstrates that the improved ResNet50 network can be used to construct an automatic pain expression recognition model for elderly patients with hip fractures, which has higher accuracy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Shuang, Liangbo, Huiwen, Jing, Xiaoying, Siyi, Xiaoya and Wen.)

The accurate recognition of emotion is important for interpersonal interaction and when navigating our social world. However, not all facial displays reflect the emotional experience currently being felt by the expresser. Indeed, faces express both genuine and posed displays of emotion. In this article, we summarize the importance of motion for the recognition of face identity before critically outlining the role of dynamic information in determining facial expressions and distinguishing between genuine and posed expressions of emotion. We propose that both dynamic information and face familiarity may modulate our ability to determine whether an expression is genuine or not. Finally, we consider the shared role for dynamic information across different face recognition tasks and the wider impact of face familiarity on determining genuine from posed expressions during real-world interactions.

In order to improve the detection accuracy of expression recognition technology, a face expression recognition method based on cascade network optimized by SWA is proposed. Compared with a single convolutional network, multi-network cascading reachs higher detection accuracy. With respect to the traditional training method SGD, SWA training method enhances the generalization ability of the sub-network in the cascade network, which further improves the overall performance of the model. By testing on the Fer2013 dataset, the experimental results shows that the detection accuracy of the network cascaded by the way of weighted summation based on SWA training method reachs 74.478%, which is 1.4% higher than the single network model trained by the traditional SGD method. In addition, the improved model proposed in the present study reachs a higher recognition accuracy than other typical methods. [ABSTRACT FROM AUTHOR]

Manual font design is difficult and requires professional knowledge and skills to perform. Therefore, how to automatically generate the required fonts is a very challenging research task. On the other hand, there are few people who have studied the relationship between fonts and emotions, and common fonts generally cannot reflect emotional information. This paper proposes an Emotional Guidance GAN: an automatic Chinese font generation framework based on Generative Adversarial Network (GAN), which enables the generated fonts to reflect human emotional information. First, an elaborated questionnaire system was developed from Tencent company, which aims to quantitatively figure out the relationship between fonts and emotions. A visual expression recognition part is designed based on the trained model to provide a font generation module with conditional information. Moreover, the Emotional Guidance GAN (EG-GAN) with EM Distance and Gradient Penalty, as well as classification strategies, is proposed to generate new fonts with combined multiple styles that infer by an expression recognition module. The results of the evaluation experiments and the resolution of the synthesized font characters show the credibility of our model. [ABSTRACT FROM AUTHOR]

The accurate recognition of emotion is important for interpersonal interaction and when navigating our social world. However, not all facial displays reflect the emotional experience currently being felt by the expresser. Indeed, faces express both genuine and posed displays of emotion. In this article, we summarize the importance of motion for the recognition of face identity before critically outlining the role of dynamic information in determining facial expressions and distinguishing between genuine and posed expressions of emotion. We propose that both dynamic information and face familiarity may modulate our ability to determine whether an expression is genuine or not. Finally, we consider the shared role for dynamic information across different face recognition tasks and the wider impact of face familiarity on determining genuine from posed expressions during real-world interactions. [ABSTRACT FROM AUTHOR]

Emotion recognition is becoming commercially popular due to the major role of analytics in various aspects of marketing and strategy management. Several papers have been proposed in emotion recognition. They are mainly classified in the past under 2D and 3D emotion recognition, out of which 2D emotion recognition has been more popular. Various aspects like facial posture, light intensity variations and sensor-independent recognition have been studied by different authors in the past. However, in reality, 3D emotion recognition has been found to be more efficient which has a broader area of use. In this paper, a 3D tracking plane with 2D feature points has enabled us to recognize emotions by statistical voting method from all planes having over threshold number of points in their respective contour area. The proposed technique's results are comparable to existing methods in terms of time, space complexity and accuracy improvement. [ABSTRACT FROM AUTHOR]