Facial Expression Recognition Based on PCD-CNN with Pose and Expression

With the work and study pressure on people increasing and the importance of mental health problems on the rise, facial expression analysis plays an important role in mental health auxiliary diagnosis and treatment (MHADT). This study proposes a facial expression localization and recognition model that integrates Main Directional Mean Optical Flow (MDMO) with the Transformer architecture. It addresses the problems of insufficient generalization ability and limited temporal modeling ability of traditional methods in facial expression recognition. The study is based on the authoritative Audio/Visual Emotion Challenge 2019 Depression Detection Sub-challenge (AVEC2019 DDS) dataset in the mental health field, which contains 163 training samples, 56 validation samples, and 56 test samples. With Explicit Shape Regression, Local Binary Patterns, Mnemonic Descent Method, Convolutional Neural Network, and benchmark models as comparison objects, it systematically evaluates the model's performance in error, accuracy, and processing speed. The results show that the model achieves the best performance in both facial expression localization and recognition tasks. The validation set errors are 7.26 and 6.85, the localization accuracy reaches 89.6%, and the recognition accuracy reaches 88.9%, which is significantly better than other methods. At the same time, its image processing time is 55 milliseconds (ms) and 44ms, balancing high precision and real-time performance. The study indicates that the fusion of MDMO and Transformer can effectively capture the spatial and temporal features of facial expressions. Thus, this fusion method provides an efficient, stable, and scalable technical solution for emotion recognition in MHADT. This improves both the FER and localization accuracy and effect. Besides, it provides a novel reference in both approaches and application level for the intelligent development of mental health evaluation.

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.

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.

In this paper, a person-specific facial expression recognition method which is based on Personal Facial Expression Space (PFES) is presented. The multidimensional scaling maps facial images as points in lower dimensions in PFES. It reflects personality of facial expressions as it is based on the peak instant of facial expression images of a specific person. In constructing PFES for a person, his/her whole normalized facial image is considered as a single pattern without block segmentation and differences of 2-D DCT coefficients from neutral facial image of the same person are used as features. Therefore, in the early part of the paper, separation characteristics of facial expressions in the frequency domain are analyzed using a still facial image database which consists of neutral, smile, anger, surprise and sadness facial images for each of 60 Japanese males (300 facial images). Results show that facial expression categories are well separated in the low frequency domain. PFES is constructed using multidimensional scaling by taking these low frequency domain of differences of 2-D DCT coefficients as features. On the PFES, trajectory of a facial image sequence of a person can be calculated in real time. Based on this trajectory, facial expressions can be recognized. Experimental results show the effectiveness of this method.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Facial expression recognition (FER) in the wild is a challenging task due to some uncontrolled factors such as occlusion, illumination, and pose variation. The current methods perform well in controlled conditions. However, there are still two issues with the in-the-wild FER task: (i) insufficient descriptions of long-range dependency of expression features in the facial information space and (ii) not finely refining subtle inter-classes distinction from multiple expressions in the wild. To overcome the above issues, an end-to-end model for FER, named attention-modulated contextual spatial information network (ACSI-Net), is presented in this paper, with the manner of embedding coordinate attention (CA) modules into a contextual convolutional residual network (CoResNet). Firstly, CoResNet is constituted by arranging contextual convolution (CoConv) blocks of different levels to integrate facial expression features with long-range dependency, which generates a holistic representation of spatial information on facial expression. Then, the CA modules are inserted into different stages of CoResNet, at each of which the subtle information about facial expression acquired from CoConv blocks is first modulated by the corresponding CA module across channels and spatial locations and then flows into the next layer. Finally, to highlight facial regions related to expression, a CA module located at the end of the whole network, which produces attentional masks to multiply by input feature maps, is utilized to focus on salient regions. Different from other models, the ACSI-Net is capable of exploring intrinsic dependencies between features and yielding a discriminative representation for facial expression classification. Extensive experimental results on AffectNet and RAF_DB datasets demonstrate its effectiveness and competitiveness compared to other FER methods.

Many Facial expression recognition methods have achieved great success, but they only considered front facial images or facial images close to the front. Besides, unlike in-the-laboratory datasets, the facial images in the real world (or in the wild) are without lighting and pose control, so that it is a big challenge to recognize these facial expressions. In this paper, the PIDViT (i.e., Pose-Invariant Distilled Vision Transformer) using the teacher-student model for the probability distributions of facial expressions of frontal and multi-pose faces was proposed and solved the pose variance and occlusion issues on expression recognition. First, the multi-pose face dataset FairFace-3D was built from the original FairFace and then used to train pose-invariance on the PIDViT. The PIDViT was trained in two stages; stage 1 is to train the PIDViT to achieve the consistency of facial expressions between frontal faces and multi-pose faces, and stage 2 is to use the student model pre-trained in stage 1 and train facial expressions further on target datasets. Finally, comprehensive experiments were conducted on three in the wild facial expression datasets, and the results validates the generalization of the PIDViT and its superiority over most state-of-the-art models.

The automatic recognition of facial expressions is challenging for many reasons, and there is still scope for research in the control of sample quality, the construction of effective feature extraction model and multi feature fusion. This study proposes a facial expression recognition method that combines transfer learning, attention mechanism, and feature fusion technology. In this method, various features are extracted from several pre-trained deep learning networks; the recognition weight of each sample is then learned via a quality attention network. This weight can be used to distinguish the quality of samples and suppress the contribution of low-quality samples in the training process. Finally, for a variety of features, this study proposes a fusion method based on feature clustering and correction. The experimental results indicate that the proposed method can significantly improve the accuracy of expression recognition and achieve state-of-the-art recognition accuracy for RAF-DB and FER2013+ datasets.

Facial expression recognition is a practical application in the field of artificial intelligence. This article focuses on the introduction of the principles of each facial expression recognition method based on deep learning in chronological order, as well as the advantages and disadvantages of each method. The methods of facial expression recognition based on deep learning introduced in this article are as follows: Li Dunyu [1] proposed a method of LBP and BP neural network to realize facial expression recognition. And then the convolutional neural network is widely used, Zhou Benjun [2] merged LBP and convolutional neural network to realize facial expression recognition; Liu [3] establish a model that combines a recurrent neural network with a 3D convolutional neural network, and added a timeline so that the machine can extract and recognize facial expressions in a video; Jung [4] proposed to combine the convolutional neural network and the deep neural network to form a new neural network DTAGN, by using this new network he completes facial expression recognition; Shi Xugan [5] proposed a model of combining deep belief network with multi-layer perceptron (MLP), and successfully applied it to the research of facial expression recognition. By utilizing joint training of feature points and multiple networks Xia Tian [6] finish the research; Zhang Feifei [7] and other scholars proposed multi-pose facial expression recognition based on generating an adversarial network.

This paper presents a discriminative temporal topic model (DTTM) for facial expression recognition. Our DTTM is developed by introducing temporal and categorical information into Latent Dirichlet Allocation (LDA) topic model. Temporal information is integrated by placing an asymmetric Dirichlet prior over document-topic distributions. The discriminative ability is improved by a supervised term weighting scheme. We describe the resulting DTTM in detail and show how it can be applied to facial expression recognition. Experiments on CMU expression database illustrate that the proposed DTTM is very effective in facial expression recognition.KeywordsFacial ExpressionTopic ModelLatent Dirichlet AllocationFacial Expression RecognitionActive Appearance ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this paper, a novel deep neural network (DNN)-driven feature learning method is proposed and applied to multi-view facial expression recognition (FER). In this method, scale invariant feature transform (SIFT) features corresponding to a set of landmark points are first extracted from each facial image. Then, a feature matrix consisting of the extracted SIFT feature vectors is used as input data and sent to a well-designed DNN model for learning optimal discriminative features for expression classification. The proposed DNN model employs several layers to characterize the corresponding relationship between the SIFT feature vectors and their corresponding high-level semantic information. By training the DNN model, we are able to learn a set of optimal features that are well suitable for classifying the facial expressions across different facial views. To evaluate the effectiveness of the proposed method, two nonfrontal facial expression databases, namely BU-3DFE and Multi-PIE, are respectively used to testify our method and the experimental results show that our algorithm outperforms the state-of-the-art methods.

Recently, dictionary learning is actively investigated in image and signal processing. A variety of dictionary learning methods for the classification problems have been proposed. In this paper, we propose a facial expression recognition system using a multi-modal motion dictionary based on motion flow composed of motion flow intensity and motion flow direction. At the dictionary learning stage, two dictionaries having different modalities are learned from motion flow intensity and from motion flow angle data of facial expression image sequences. We made a feature vector by concatenating two weight vectors of individual dictionaries from each image sequence for classification. Experimental result shows higher accuracy than conventional reconstruction-based approaches with extremely reduced classification time. The proposed approach is a promising method for real-time facial expression recognition from motion flow data.

Recently, dictionary learning is actively investigated in image and signal processing. A variety of dictionary learning methods for the classification problems have been proposed. In this paper, we propose a facial expression recognition system using a multi-modal motion dictionary based on motion flow composed of motion flow intensity and motion flow direction. At the dictionary learning stage, two dictionaries having different modalities are learned from motion flow intensity and from motion flow angle data of facial expression image sequences. We made a feature vector by concatenating two weight vectors of individual dictionaries from each image sequence for classification. Experimental result shows higher accuracy than conventional reconstruction-based approaches with extremely reduced classification time. The proposed approach is a promising method for real-time facial expression recognition from motion flow data.

A Facial Expression Recognition (FER) method based on Conv3D-ConvLSTM-SEnet in an online education environment is proposed to address the issue of low accuracy in current classroom expression recognition (FER) methods. Firstly, ConvLSTM is used to integrate the local feature extraction ability of CNN and the temporal modeling ability of LSTM, and based on this, the rich spatial features of the image are characterized. Then, by introducing Depth Separated Convolution (DSC) to change the number of output channels and separate each channel, the Feature Maps (F-M) are sequentially concatenated to obtain a multi-channel output F-M. Finally, based on ConvLSTM and SEnet modules, a Conv3D ConvLSTM-based FER model was proposed by redistributing the extracted abstract features and basic texture features, achieving high accuracy classroom FER. The proposed classroom FER method and the other five methods were compared and analyzed through simulation experiments using the CK[Formula: see text], FER2013 and JAFFE datasets, respectively. The results indicate that the proposed method achieves the highest accuracy, precision, recall, and F1 score, with improvements of at least 1.85%, 2.41%, 1.18% and 2.05%, respectively, compared to the other five methods on the FER2013 dataset. The proposed method can perform FER on students in online classroom teaching, detect their emotions in real-time, and help teachers make better course teaching adjustments based on this.

This paper proposes a method of facial expression recognition based on Zernike moments and the minimum classification error (MCE) based hidden Markov model (HMM). In the feature extraction of face, the method of Zernike moments feature extraction based on local feature regions is adopted. First, eyes and mouths are segmented from the facial expression image and Zernike moments feature vectors of eyes and mouth are extracted. In the classification of expression, using MCE criteria is adopted to improve the performance of HMM. The results show that the performance of expression recognition system can be improved to some extent by using Zernike moments and MCE criterion.

Facial expression recognition across domains, e.g., training and testing facial images come from different facial poses, is very challenging due to the different marginal distributions between training and testing facial feature vectors. To deal with such challenging cross-domain facial expression recognition problem, a novel transductive transfer subspace learning method is proposed in this paper. In this method, a labelled facial image set from source domain is combined with an unlabelled auxiliary facial image set from target domain to jointly learn a discriminative subspace and make the class labels prediction of the unlabelled facial images, where a transductive transfer regularized least-squares regression (TTRLSR) model is proposed to this end. Then, based on the auxiliary facial image set, we train a SVM classifier for classifying the expressions of other facial images in the target domain. Moreover, we also investigate the use of color facial features to evaluate the recognition performance of the proposed facial expression recognition method, where color scale invariant feature transform (CSIFT) features associated with 49 landmark facial points are extracted to describe each color facial image. Finally, extensive experiments on BU-3DFE and Multi-PIE multiview color facial expression databases are conducted to evaluate the cross-database & cross-view facial expression recognition performance of the proposed method. Comparisons with state-of-the-art domain adaption methods are also included in the experiments. The experimental results demonstrate that the proposed method achieves much better recognition performance compared with the state-of-the-art methods.