Facial Expression Recognition Algorithm Integrating Semantic Features and Attention Mechanisms

It is fascinating to hear about the research conducted on facial expression recognition algorithms and its significance in the era of intelligence, especially with the development of the internet and IoT. The use of deep learning method and neural network VGG to study facial expressions sounds promising. It is encouraging to know that the network structure was improved and pooled using Soft pool, thereby enhancing the extraction of facial expression features. The integration of an attention mechanism to improve the ability of the network to recognize facial expressions appears to be a valuable addition to the research. it is impressive that the recognition rate of the improved network on the face expression dataset RAF-DB reached 86.1%, exceeding that of the original VGG network on this data set. this outcome suggests that the changed network is effective for facial expression recognition. thank you for sharing this information.

We proposed a facial motion tracking and expression recognition system based on video data. By a 3D deformable facial model, the online statistical model (OSM) and cylinder head model (CHM) were combined to track 3D facial motion in the framework of particle filtering. For facial expression recognition, a fast and efficient algorithm and a robust and precise algorithm were developed. With the first, facial animation and facial expression were retrieved sequentially. After that facial animation was obtained, facial expression was recognized by static facial expression knowledge learned from anatomical analysis. With the second, facial animation and facial expression were simultaneously retrieved to increase the reliability and robustness with noisy input data. Facial expression was recognized by fusing static and dynamic facial expression knowledge, the latter of which was learned by training a multi-class expressional Markov process using a video database. The experiments showed that facial motion tracking by OSM+CHM is more pose robust than that by OSM, and the facial expression score of the robust and precise algorithm is higher than those of other state-of-the-art facial expression recognition methods.

Facial expression methods play a vital role in human-computer interaction and other fields, but there are factors such as occlusion, illumination, and pose changes in wild facial recognition, as well as category imbalances between different datasets, that result in large variations in recognition rates and low accuracy rates for different categories of facial expression datasets. This study introduces RCL-Net, a method of recognizing wild facial expressions that is based on an attention mechanism and LBP feature fusion. The structure consists of two main branches, namely the ResNet-CBAM residual attention branch and the local binary feature (LBP) extraction branch (RCL-Net). First, by merging the residual network and hybrid attention mechanism, the residual attention network is presented to emphasize the local detail feature information of facial expressions; the significant characteristics of facial expressions are retrieved from both channel and spatial dimensions to build the residual attention classification model. Second, we present a locally improved residual network attention model. LBP features are introduced into the facial expression feature extraction stage in order to extract texture information on expression photographs in order to emphasize facial feature information and enhance the recognition accuracy of the model. Lastly, experimental validation is performed using the FER2013, FERPLUS, CK+, and RAF-DB datasets, and the experimental results demonstrate that the proposed method has superior generalization capability and robustness in the laboratory-controlled environment and field environment compared to the most recent experimental methods.

Facial expression recognition algorithm using LGC based on horizontal and diagonal prior principle

In this paper, we propose a novel approach for facial expression decomposition - higher-order singular value decomposition (HOSVD), a natural generalization of matrix SVD. We learn the expression subspace and person subspace from a corpus of images showing seven basic facial expressions, rather than resort to expert-coded facial expression parameters. We propose a simultaneous face and facial expression recognition algorithm, which can classify the given image into one of the seven basic facial expression categories, and then other facial expressions of the new person can be synthesized using the learned expression subspace model. The contributions of this work lie mainly in two aspects. First, we propose a new HOSVD based approach to model the mapping between persons and expressions, used for facial expression synthesis for a new person. Second, we realize simultaneous face and facial expression recognition as a result of facial expression decomposition. Experimental results are presented that illustrate the capability of the person subspace and expression subspace in both synthesis and recognition tasks. As a quantitative measure of the quality of synthesis, we propose using gradient minimum square error (GMSE) which measures the gradient difference between the original and synthesized images.

Facial expression recognition is one of the research hotspots in the field of computer vision. Aiming at the problems that the current machine learning method extracts facial features are less robust and the traditional convolutional neural network can not fully extract the expression features, a residual network model integrating CBAM attention mechanism is proposed. Given an intermediate feature map, a attention map is generated on the channel domain and the spatial domain of the feature map respectively by our module, and multiplied by the original feature map to obtain a recalibrated feature map. In the training process, the improved loss function A-Softmax is used to generate the angular interval by manipulating the feature surface, so that the different class features learned have angular intervals in the angular space, and the similar features are more closely clustered. Experiments on FER2013 and JAFFE dataset show that the proposed method effectively improves the feature expression ability of the network, enhances the ability to distinguish different facial expression features, and achieves good recognition performance.

In order to improve the recognition of facial expression recognition rate, facial expression recognition algorithm is based on a LBP and SVM decision tree. First will facial expression image is converted to LBP characteristic spectrum using LBP algorithm, and then the LBP characteristic spectrum into LBP histogram feature sequence, finally completes the classification and recognition of facial expression by SVM decision tree algorithm, and prove the effectiveness of the proposed method in the recognition of facial expression database in JAFFE.

In order to solve the problems of low average gradient and long recognition time in traditional facial expression recognition method, a multi-scale detail enhancement method for facial dynamic expression recognition of sprint athletes is proposed. A principal component analysis method was used to establish the facial expression feature subspace of sprinters, to project and reduce the dimension of the facial dynamic expression feature vector of sprinters, and to obtain the low frequency information and high frequency information of the facial image of sprinters by bilateral filtering. The multi-scale details of expression are enhanced by using side suppression network model and improving image S curve. The feature vector of facial dynamic expression is input into support vector machine to recognise the facial dynamic expression of sprinter. Experimental results show that the average value of annoying gradient is about 98 and the shortest time s. is about 1.9.

Facial expression recognition (FER), applied in fields such as interaction and intelligent security, has seen widespread development with the advancement of machine vision technology. However, in natural environments, faces are often obscured by masks, posture, and body parts, leading to incomplete features, which results in poor accuracy of existing facial expression recognition algorithms. Apart from extreme scenarios where facial features are completely blocked, the key information of facial expression features is mostly preserved in most cases, yet insufficient parsing of these features leads to poor recognition results. To address this, we propose a novel joint learning framework that integrates explicit occlusion parsing and feature enhancement. Our model consists of three core modules: a Facial Occlusion Parsing Module (FOPM) for real-time occlusion estimation, an Expression Feature Fusion Module (EFFM) for integrating appearance and geometric features, and a Facial Expression Recognition Module (FERM) for final classification. Extensive experiments under a rigorous and reproducible protocol demonstrate significant improvements of our approach. On the masked facial expression datasets RAF-DB and FER+, our model achieves accuracies of 91.24% and 90.18%, surpassing previous state-of-the-art methods by 2.62% and 0.96%, respectively. Additional evaluation on a real-world masked dataset with diverse mask types further confirms the robustness and generalizability of our method, where it attains an accuracy of 89.75%. Moreover, the model maintains high computational efficiency with an inference time of 12.4 ms per image. By effectively parsing and integrating partially obscured facial features, our approach enables more accurate and robust expression recognition, which is essential for real-world applications in interaction and intelligent security systems.

A novel database of children's spontaneous facial expressions (LIRIS-CSE)

In this paper, we proposed an algorithm for facial expression recognition based on wavelet transform and SOM and MLP neural network. The algorithm first uses discrete wavelet transform to extract feature vectors and then uses SOM neural network to further reduce data dimension. After that, An MLP neural network is used as a facial expression classifier. In the end, some experiment results are given to show the performance of the proposed.

With the rise of artificial intelligence, machine learning (ML) is increasingly integrated into daily life. Facial expressions, lasting about 1/20th of a second and difficult to conceal, convey nuanced emotions beyond words. They are categorized as macro expressions, displayed under normal circumstances, and micro expressions, fleeting and subconscious. However, recognizing expressions in photos is challenging due to varied backgrounds, appearances, age, and race, impacting accuracy. Addressing this, our research focuses on a machine learning-based facial expression recognition algorithm. Our experimental results, using CASIA and NTERFACE databases, demonstrate that ML outperforms KNN and ANN with recognition rates 41.54% and 26.94% higher, respectively. By adaptively learning global and local features from complete expression images and face parts, our algorithm offers practical and significant advancements in expression recognition.

In this paper, we proposed a novel algorithm for Facial Expression Recognition (FER) which was based on fusion of gabor texture features and Local Phase Quantization (LPQ). Firstly, the LPQ feature and gabor texture feature were respectively extracted from every expression image. LPQ features are histograms of LPQ transform. Five scales and eight orientations of gabor wavelet filters are used to extract gabor texture features and adaboost algorithm is used to select gabor features. Then we obtain two expression recognition results on both expression features by Sparse Representation-based Classification (SRC) method. Finally, the final expression recognition was performed by fusion of residuals of two SRC algorithms. The experiment results on Japanese Female Facial Expression (JAFFE) database demonstrated that the new algorithm was better than the original two algorithms, and this algorithm had a much higher recognition rate than the traditional algorithm.

In this paper we focus on the need for seeking a robust low-rank linear regression algorithm for facial expression recognition. Motivated by low-rank matrix recovery, we assumed that the matrix whose data are from the same pattern as columns vectors is approximately low-rank. The proposed algorithm firstly decomposes the training images per class into the sum of the sparse error matrix, the low-rank matrix of the original images and the class discrimination criterion. Then accelerated proximal gradient algorithm was used to minimize the sum of l1-norm and the nuclear matrix norm to get the set of tight linear regression base as the dictionary. Finally, we reconstruct the samples by tight dictionary and classified the face image by linear regression method according to the residual. The experimental results on facial expression databases show that the proposed method works well.

Aiming at the problems of huge parameters and network degradation caused by simple linear stacked convolution layers or continuous full connection layers in traditional expression recognition methods, two convolution neural network models are designed through depth separation convolution and residual module respectively to widen and deepen the network. Firstly, model A adopts depth separation convolution instead of regular convolution layer, and the global average pooling layer replaces the final full connection layer, utilizes the methods of dropout, batch normalization, activation function of PReLU and image augmentation to avoid over-fitting effectively. Model B adopts pre-trained ResNet50 model to extract facial features, magnifies the images twice by the SRGAN method. Using ensemble method to fuse model A and B, the accuracy is further improved. To verify the feasibility of the method, the model was tested on the FER2013 facial expression dataset, and the performance was compared with the other facial expression recognition algorithms. The final results showed the improved convolutional neural network (CNN) reached the advanced precision of 73.244% in FER2013 dataset, and the experiment data and the number of model parameters all proved the effectiveness of this method.