Expression Recognition Model Research Articles

Does the face expression recognition model under the online entrepreneurial education model important for deep learning technologies and entrepreneurial emotions?

AI's rapid development has already affected the economy and society, and it might revolutionize how goods and services are manufactured and categorized. This affects the country's production, employment rate, and competitiveness. This research aims to determine how emotions (good and negative) impact deep learning technologies and company startup intentions. The research also examined how emotion recognition influences online entrepreneurship education and how deep learning and entrepreneurship are connected. In conclusion, the research is one of the pioneering efforts that provides a comprehensive grasp of deep learning and facial recognition in entrepreneurship. 78 of the 650 replies were insufficient, according to the statistics. Some individuals constantly picked 3, while others submitted blank questionnaires. The answer is inadequate. 572 surveys were utilized for the study. Positive emotions affect the urge to establish a company, according to the research. People who are generally happy may be able to develop their entrepreneurial expertise and be more driven to behave entrepreneurially. Positive and negative emotions have a significant impact on the deep learning technology and entrepreneurial intention connection. The findings imply that individuals who often experience pleasant emotions may be better able to improve their entrepreneurial cognition and, as a consequence, their drive to engage in entrepreneurial activity. The facial recognition model in entrepreneurship significantly moderates the relationship between deep learning technology and entrepreneurial intention (=0.262, p 0.001); hence, hypotheses 6, 7, and 8 are validated. This provides support for the hypothesized results, claiming that facial recognition plays a significant role in determining the relationship between deep learning technology and entrepreneurial intention. The results would be significant for university students, academicians, and policymakers as they seek to link practices and outcomes. The study is among the pioneers on issues related to deep learning mechanisms in eastern China.

Cross-Domain Facial Expression Recognition through Reliable Global–Local Representation Learning and Dynamic Label Weighting

Cross-Domain Facial Expression Recognition (CD-FER) aims to develop a facial expression recognition model that can be trained in one domain and deliver consistent performance in another. CD-FER poses a significant challenges due to changes in marginal and class distributions between source and target domains. Existing methods primarily emphasize achieving domain-invariant features through global feature adaptation, often neglecting the potential benefits of transferable local features across different domains. To address this issue, we propose a novel framework for CD-FER that combines reliable global–local representation learning and dynamic label weighting. Our framework incorporates two key modules: the Pseudo-Complementary Label Generation (PCLG) module, which leverages pseudo-labels and complementary labels obtained using a credibility threshold to learn domain-invariant global and local features, and the Label Dynamic Weight Matching (LDWM) module, which assesses the learning difficulty of each category and adaptively assigns corresponding label weights, thereby enhancing the classification performance in the target domain. We evaluate our approach through extensive experiments and analyses on multiple public datasets, including RAF-DB, FER2013, CK+, JAFFE, SFW2.0, and ExpW. The experimental results demonstrate that our proposed model outperforms state-of-the-art methods, with an average accuracy improvement of 3.5% across the five datasets.

Mathematical expression recognition using a new deep neural model

In this paper, we propose a novel deep neural model for Mathematical Expression Recognition (MER). The proposed model uses encoder–decoder transformer architecture that is supported by additional pre/post-processing modules, to recognize the image of mathematical formula and convert it to a well-formed language. A novel pre-processing module based on domain prior knowledge is proposed to generate random pads around the formula’s image to create more efficient feature maps and keeps all the encoder neurons active during the training process. Also, a new post-processing module is developed which uses a sliding window to extract additional position-based information from the feature map, that is proved to be useful in the recognition process. The recurrent decoder module uses the combination of feature maps and the additional position-based information, which takes advantage of a soft attention mechanism, to extract the formula context into the LaTeX well-formed language. Finally, a novel Reinforcement Learning (RL) module processes the decoder output and tunes its results by sending proper feedbacks to the previous steps. The experimental results on im2latex-100k benchmark dataset indicate that each devised pre/post-processing as well as the RL refinement module has a positive effect on the performance of the proposed model. The results also demonstrate the higher accuracy of the proposed model compared to the state-of-the-art methods.

Multiscale Convolutional Attention-based Residual Network Expression Recognition

<p>Expression recognition has wide application in the fields of distance education and clinical medicine. In response to the problems of insufficient feature extraction ability of expression recognition models in current research, and the deeper the depth of the model, the more serious the loss of useful information, a residual network model with multi-scale convolutional attention is proposed. This model mainly takes the residual network as the main body, adds normalization layer and channel attention mechanism, so as to extract useful image information at multiple scales, and incorporates the Inception module and channel attention module into the residual network to enhance the feature extraction ability of the model and to prevent the loss of more useful information due to too deep network, and to improve the generalization performance of the model. From results of lots of experiments we can see that the recognition accuracy of the model in FER+ and CK+ datasets reaches 87.80% and 99.32% respectively, with better recognition performance and robustness.</p> <p> </p>

Prediction of Impulsive Aggression Based on Video Images.

In response to the subjectivity, low accuracy, and high concealment of existing attack behavior prediction methods, a video-based impulsive aggression prediction method that integrates physiological parameters and facial expression information is proposed. This method uses imaging equipment to capture video and facial expression information containing the subject's face and uses imaging photoplethysmography (IPPG) technology to obtain the subject's heart rate variability parameters. Meanwhile, the ResNet-34 expression recognition model was constructed to obtain the subject's facial expression information. Based on the random forest classification model, the physiological parameters and facial expression information obtained are used to predict individual impulsive aggression. Finally, an impulsive aggression induction experiment was designed to verify the method. The experimental results show that the accuracy of this method for predicting the presence or absence of impulsive aggression was 89.39%. This method proves the feasibility of applying physiological parameters and facial expression information to predict impulsive aggression. This article has important theoretical and practical value for exploring new impulsive aggression prediction methods. It also has significance in safety monitoring in special and public places such as prisons and rehabilitation centers.

Facial Expression Recognition Model Based on CNN and Data Augmentation Method

Face expressions are essential in expressing human emotions, and it is accomplished by separating features and categorizing them. Facial expression recognition technology has been widely employed in human-computer interaction, telemedicine, mental health, and criminal investigation detection. In recent years, significant advances in deep learning have facilitated the development of facial expression recognition, making it increasingly accessible. Convolutional neural networks (CNN) are the foundation of the face expression recognition model presented in this article. In order to maximize the final accuracy, an abundance of sample photographs are required for training and optimization. As a result, the 35,886 facial expression photos from the FER2013 dataset, which contains all seven emotions, were used for both training and testing. The photos were scaled down to 4848 pixels during data preparation, and data augmentation was carried out. To get accurate face expression recognition results, various optimizers and parameters were chosen for the training network, which was a bespoke structure based on the VGG network design. The model constructed in this study achieved an accuracy of 73.16% during prediction.

FACE EXPRESSION RECOGNIZER DENGAN CONVOLUTIONAL NEURAL NETWORK UNTUK MEMBANTU PENDERITA AUTISME MENGENALI EKSPRESI WAJAH SESEORANG

Autism is a type of mental health disorder that is 80% caused by heredity, and environmental influences cause the rest. People with autism tend to be unable to concentrate and look at the other person when interacting. Since the age of toddlers, people with autism have a shallow response to the surrounding environment. In addition, people with autism also find it very difficult to recognize someone's expression, even though facial expressions or facial expressions are one way that can be used to recognize someone's emotions. In addition, facial expressions indirectly reveal the contents of a person's thoughts. To overcome this, the author wants to build a face expression recognition model to help people with autism recognize someone's facial expressions. The primary purpose of this research is to help people with autism socialize and recognize the facial expressions of the people around them. This face expression recognition model was built by applying Convolutional Neural Network (CNN) intelligence and using the Tensorflow library and the Keras API. The dataset used in this study is a collection of faces from all over the world. In this research and model development process, the output display of the detection of facial expressions is in the form of diagrams and descriptions of the expressions that a person is experiencing.

Hybrid heuristic mechanism for occlusion aware facial expression recognition scheme using patch based adaptive CNN with attention mechanism

In computer vision, the recognition of expressions from partially occluded faces is one of the serious problems. By the prior recognizing techniques it can solve the issue by various assumptions. A benchmark-guided branch was proposed for detecting and eliminating the manipulated features from the occluded regions since the human visual system is proficient for eliminating the occlusion and the appropriate focal point was obtained on the non-occluded areas. In recent years deep learning has attained a great place in the recognition of facial reactions Still, the precision of facial expression is affected by the occlusion and large skew. In this research work, a deep structure-based occlusionaware facial expression recognition mechanism is introduced to provide superior recognition results. Firstly, the required image is taken from publically provided online sources and the gathered images are subjected to the face extraction method. The face extraction method is done via the ViolaJones method for the extraction of redundant patterns from the original images. Secondly, the extracted face features are given to the pattern recognition stage, where the Adaptive CNN with Attention Mechanism (ACNN-AM) is introduced. This mechanism automatically forms the occluded region of the face and the focal point was on the most discriminative un-occluded regions. Moreover, the hidden patterns in the Occlusion aware facial expressions are identified through the Hybrid Galactic Swarm Yellow Saddle Goatfish Optimization (HGSYSGO). Finally, the overall effectiveness of the developed occlusion aware facial expression recognition model is examined through the comparative analysis of different existing baseline recognition techniques.

A novel driver emotion recognition system based on deep ensemble classification

Driver emotion classification is an important topic that can raise awareness of driving habits because many drivers are overconfident and unaware of their bad driving habits. Drivers will acquire insight into their poor driving behaviors and be better able to avoid future accidents if their behavior is automatically identified. In this paper, we use different models such as convolutional neural networks, recurrent neural networks, and multi-layer perceptron classification models to construct an ensemble convolutional neural network-based enhanced driver facial expression recognition model. First, the faces of the drivers are discovered using the faster region-based convolutional neural network (R-CNN) model, which can recognize faces in real-time and offline video reliably and effectively. The feature-fusing technique is utilized to integrate the features extracted from three CNN models, and the fused features are then used to train the suggested ensemble classification model. To increase the accuracy and efficiency of face detection, a new convolutional neural network block (InceptionV3) replaces the improved Faster R-CNN feature-learning block. To evaluate the proposed face detection and driver facial expression recognition (DFER) datasets, we achieved an accuracy of 98.01%, 99.53%, 99.27%, 96.81%, and 99.90% on the JAFFE, CK+, FER-2013, AffectNet, and custom-developed datasets, respectively. The custom-developed dataset has been recorded as the best among all under the simulation environment.

Facial emotion recognition using deep learning detector and classifier

Numerous research works have been put forward over the years to advance the field of facial expression recognition which until today, is still considered a challenging task. The selection of image color space and the use of facial alignment as preprocessing steps may collectively pose a significant impact on the accuracy and computational cost of facial emotion recognition, which is crucial to optimize the speed-accuracy trade-off. This paper proposed a deep learning-based facial emotion recognition pipeline that can be used to predict the emotion of detected face regions in video sequences. Five well-known state-of-the-art convolutional neural network architectures are used for training the emotion classifier to identify the network architecture which gives the best speed-accuracy trade-off. Two distinct facial emotion training datasets are prepared to investigate the effect of image color space and facial alignment on the performance of facial emotion recognition. Experimental results show that training a facial expression recognition model with grayscale-aligned facial images is preferable as it offers better recognition rates with lower detection latency. The lightweight MobileNet_v1 is identified as the best-performing model with WM=0.75 and RM=160 as its hyper-parameters, achieving an overall accuracy of 86.42% on the testing video dataset.

Facial expression recognition for a seven-class small and medium-sized dataset based on transfer learning CNNs

As one of the most common biological information for human to express their emotions, facial expression plays an important role in biological research, psychological analysis and even human-computer interaction in the computer field. It is very important to use the efficient computing and processing power of computers to realize automatic facial expression recognition. However, in the research process of this field, most people's technology and model achievements are based on large datasets, which lack the universality of small and medium-sized datasets. Therefore, this project provides an optimization model on a specific seven-class small and medium face image dataset and provides a possible technical optimization reference direction for facial expression recognition models on similar small and medium-sized datasets. During the experiment, the training performance of VGG16 and MobileNet is compared. A comparative experiment is set up to observe the effect of transfer learning mechanism on training results. The results show that transfer learning has a significant effect on the performance of the model, and the accuracy of the optimal test set is more than 90%. Regardless of whether transfer learning mechanism is used, the training performance of VGG16 model structure is better than that of MobileNet structure in the same dataset.

Triple attention feature enhanced pyramid network for facial expression recognition

The purpose of facial expression recognition is to capture facial expression features from static pictures or videos and to provide the most intuitive information about human emotion changes for artificial intelligence devices to use effectively for human-computer interaction. Among the factors, the excessive loss of locally valid information and the irreversible degradation trend of the information at different expression semantic scales with increasing network depth are the main challenges faced currently. To address such problems, an enhanced pyramidal network model combining with triple attention mechanisms is designed in this paper. Firstly, three attention mechanism modules, i.e. CBAM, SK, and SE, are embedded into the backbone network model in stages, and the key features are sensed by using spatial or channel information mining, which effectively reduces the effective information loss caused by the network depth. Then, the pyramid network is used as an extension of the backbone network to obtain the semantic information of expression features across scales. The recognition accuracy reaches 96.25% and 73.61% in the CK+ and Fer2013 expression change datasets, respectively. Furthermore, by comparing with other current advanced methods, it is shown that the proposed network architecture combining with the triple attention mechanism and multi-scale cross-information fusion can simultaneously maintain and improve the information mining ability and recognition accuracy of the facial expression recognition model.

Students’ Facial Expression Recognition Based on Multi-head Attention Mechanism

As there are a far greater number of students in the classroom than teachers, it is difficult for teachers to grasp the learning of all students. Especially during the epidemic period, when online teaching becomes a trend, this problem is more prominent. The issue of how to grasp the learning of each student more comprehensively remains a problem to be solved in teaching. Under such circumstances, students’ facial expression recognition emerges as one of the most important solutions. In this paper, an improved facial expression recognition model based on the multi-head attention mechanism is proposed. The model is tested on two student expression databases, the JAFFE and the OL-SFED, and the recognition rate reaches 99.5% and 100%, respectively. In addition, to compare it with the models developed by other researchers, it is also tested on the RAF-DB, recording the best recognition rate of 90.35% and an average recognition rate of 83.66%, which represents the best level so far.

A multi-channel convolutional neural network based on attention mechanism fusion for facial expression recognition

Abstract Facial expressions can reflect people’s inner emotions to a certain extent, and studying facial expressions can help psychologists capture expression information in time and understand patients’ psychological changes quickly. In this paper, we establish a multi-channel convolutional neural network face expression recognition model based on the fusion of the attention mechanism. With the help of the attention mechanism and multi-channel convolutional neural network, we input expression images and perform average pooling and maximum pooling, output the features with high recognition after pooling, and identify the features with high recognition in expression images throughout the process. And with the help of multi-scale feature fusion, we improve the detection of subtle changes, such as the corners of the mouth and the eyes of the expression image target. The loss function is used to calculate the loss rate of facial expression images, which leads to the correct rate of facial expression recognition by a multi-channel convolutional neural network based on the fusion of attention mechanisms. It is demonstrated that the highest recognition correct rate of the multi-channel convolutional neural network faces expression recognition model with attention mechanism fusion is 93.56% on the FER2013 dataset, which is higher than that of the MHBP model by 23.2%. The highest correct recognition rate on the RAF-DB dataset is 91.34%, which is higher than the SR-VGG19 model by 19.39%. This shows that the multi-channel convolutional neural network face expression recognition based on the fusion of attention mechanisms improves the correct rate of facial expression recognition, which is beneficial to the research and development of psychology.

A Region Group Adaptive Attention Model For Subtle Expression Recognition

A Region Group Adaptive Attention Model For Subtle Expression Recognition

CNN-based Model for Face Expression Recognition

Face recognition is a biometric technique that uses data on facial features to identify individuals. It is also a key area of study for computer vision researchers. CNN is a subclass of feedforward neural networks with convolutional processing and depth structure and one of the illustrative deep learning techniques. Since the deep learning theory was put forth and computational power increased, CNN has rapidly advanced and is now utilized in computer vision, natural language processing, and other fields. Our research is focused on face recognition, and because the mini-Xception model has a condensed volume and few parameters, it is used in this study. The dataset we used is fer2013, which is a classical dataset among CNN algorithms and is used in many studies. We also used data augmentation methods, and Keras’ ImageDataGenerator image generator was the optimal data augmentation method we came up with after reading the paper. Finally, we came up with a final model with 61% accuracy, which we are satisfied with and within the error results of the papers we reviewed.

Online facial expression recognition based on graph convolution and long short memory networks

Video‐based facial expression recognition (FER) models have achieved higher accuracy with more computation, which is not suitable for online deployment in mobile intelligent terminals. Facial landmarks can model facial expression changes with their spatial location information instead of texture features. But classical convolution operation cannot make full use of landmark information. To this end, in this paper, we propose a novel long short memory network (LSTM) by embedding graph convolution named GELSTM for online video‐based FER in mobile intelligent terminals. Specifically, we construct landmark‐based face graph data from the client. On the server side, we introduce graph convolution which can effectively mine spatial dependencies information in a landmark‐based facial graph. Moreover, the extracted landmark's features are fed to LSTM for temporal feature aggregation. We conduct experiments on the facial expression dataset and the results show our proposed method shows superior performance compared to other deep models.

Performance Evaluation of Fusion Based Efficient Algorithm for Facial Expression Recognition

Objectives: To develop face expression recognition system using JAFFE database and to evaluate the performance of the face expression recognition models. Methods: This study used the FER model based on modified-HoG (Histogram of oriented gradient), LBP (Local Binary Patterns) and Fast Key point detector and BRIEF descriptor (FKBD) to extract the significant features of JAFFE dataset. The features extracted using HoG, LBP and FKBD techniques form a feature vector. Then, the fusion of all the features is carried out at the feature level. The multiclass SVM and KNN classifiers are used to recognize the facial expressions, effectively. Findings: In this work, an effort is made to develop a robust FER model using JAFFE database. It is recorded that, based on the experimental results, the proposed model suits better with a performance rate of 98.26% for SVM and 96.51% for KNN, when compared with the different state-of-the-art methods. Novelty: Many FER models have been developed and adopted for enhancing their quality and to extract the facial features using transform and frequency domains. It is observed that, maximum approaches are based on generating the texture features. The fusion at the feature level using modified HoG, LBP and FKBD is performed and the SVM model is more compatible when compared with other classifiers and it supports one-to-one and one-to-many comparisons’ technique. Keywords: Face Expression Recognition; Local Binary Pattern; Emotions; Nearest Neighbor; Histogram of Gradients

A Method for Analyzing Learning Sentiment Based on Classroom Time‐Series Images

With the development of smart classrooms, analyzing students’ emotions for classroom learning is an effective means of accurately capturing their learning process. Although facial expression‐based emotion analysis methods are effective in analyzing classroom learning emotions, current research focuses on facial expressions and does not consider the fact that expressions in different postures do not represent the same emotions. To provide a continuous and deeper understanding of students’ learning emotions, this study proposes an algorithm to characterize learning emotions based on classroom time‐series image data. First, face expression data for classroom scenarios are established to address the lack of expression databases in real teaching environments. Second, to improve the accuracy of facial expression recognition, a residual channel cross transformer masking net expression recognition model is proposed in this paper. Finally, to address the problem that the existing research dimension of learning emotion is too single, this paper uses the facial expression and head posture data obtained from deep learning models for fusion analysis and innovatively proposes a Dempster–Shafer evidence‐theoretic fusion model to characterize the learning emotion within the lecture duration of knowledge points. The experiments show that both the proposed expression recognition model and the learning sentiment analysis algorithm have good performance, with the expression recognition model achieving an accuracy of 73.58% on the FER2013 dataset. The proposed learning emotion analysis method provides technical support for holistic analysis of student learning effects and evaluation of students’ level of understanding of the knowledge points.

Knowledge Conditioned Variational Learning for One-Class Facial Expression Recognition.

The openness of application scenarios and the difficulties of data collection make it impossible to prepare all kinds of expressions for training. Hence, detecting expression absent during the training (called alien expression) is important to enhance the robustness of the recognition system. So in this paper, we propose a facial expression recognition (FER) model, named OneExpressNet, to quantify the probability that a test expression sample belongs to the distribution of training data. The proposed model is based on variational auto-encoder and enjoys several merits. First, different from conventional one class classification protocol, OneExpressNet transfers the useful knowledge from the related domain as a constraint condition of the target distribution. By doing so, OneExpressNet will pay more attention to the descriptive region for FER. Second, features from both source and target tasks will aggregate after constructing a skip connection between the encoder and decoder. Finally, to further separate alien expression from training expression, empirical compact variation loss is jointly optimized, so that training expression will concentrate on the compact manifold of feature space. The experimental results show that our method can achieve state-of-the-art results in one class facial expression recognition on small-scale lab-controlled datasets including CFEE and KDEF, and large-scale in-the-wild datasets including RAF-DB and ExpW.