Network For Facial Expression Recognition Research Articles

A Lightweight Model Enhancing Facial Expression Recognition with Spatial Bias and Cosine-Harmony Loss

This paper proposes a facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model effectively balances model complexity and recognition accuracy. It has two key components: a lightweight feature extraction network (LFN) and a Spatial Bias (SB) module for aggregating global information. The LFN introduces combined channel operations and depth-wise convolution techniques, effectively reducing the number of parameters while enhancing feature representation capability. The Spatial Bias module enables the model to focus on local facial features while capturing the dependencies between different facial regions. Additionally, a new loss function called Cosine-Harmony Loss is designed. This function optimizes the relative positions of feature vectors in high-dimensional space, resulting in better feature separation and clustering. Experimental results on the AffectNet and RAF-DB datasets demonstrate that the LFNSB model achieves competitive recognition accuracy, with 63.12% accuracy on AffectNet-8, 66.57% accuracy on AffectNet-7, and 91.07% accuracy on RAF-DB, while significantly reducing the model complexity.

Generative Adversarial Networks for Facial Expression Recognition in the Wild

Generative Adversarial Networks for Facial Expression Recognition in the Wild

TriCAFFNet: A Tri-Cross-Attention Transformer with a Multi-Feature Fusion Network for Facial Expression Recognition.

In recent years, significant progress has been made in facial expression recognition methods. However, tasks related to facial expression recognition in real environments still require further research. This paper proposes a tri-cross-attention transformer with a multi-feature fusion network (TriCAFFNet) to improve facial expression recognition performance under challenging conditions. By combining LBP (Local Binary Pattern) features, HOG (Histogram of Oriented Gradients) features, landmark features, and CNN (convolutional neural network) features from facial images, the model is provided with a rich input to improve its ability to discern subtle differences between images. Additionally, tri-cross-attention blocks are designed to facilitate information exchange between different features, enabling mutual guidance among different features to capture salient attention. Extensive experiments on several widely used datasets show that our TriCAFFNet achieves the SOTA performance on RAF-DB with 92.17%, AffectNet (7 cls) with 67.40%, and AffectNet (8 cls) with 63.49%, respectively.

RS-Xception: A Lightweight Network for Facial Expression Recognition

Facial expression recognition (FER) utilizes artificial intelligence for the detection and analysis of human faces, with significant applications across various scenarios. Our objective is to deploy the facial emotion recognition network on mobile devices and extend its application to diverse areas, including classroom effect monitoring, human–computer interaction, specialized training for athletes (such as in figure skating and rhythmic gymnastics), and actor emotion training. Recent studies have employed advanced deep learning models to address this task, though these models often encounter challenges like subpar performance and an excessive number of parameters that do not align with the requirements of FER for embedded devices. To tackle this issue, we have devised a lightweight network structure named RS-Xception, which is straightforward yet highly effective. Drawing on the strengths of ResNet and SENet, this network integrates elements from the Xception architecture. Our models have been trained on FER2013 datasets and demonstrate superior efficiency compared to conventional network models. Furthermore, we have assessed the model’s performance on the CK+, FER2013, and Bigfer2013 datasets, achieving accuracy rates of 97.13%, 69.02%, and 72.06%, respectively. Evaluation on the complex RAF-DB dataset yielded an accuracy rate of 82.98%. The incorporation of transfer learning notably enhanced the model’s accuracy, with a performance of 75.38% on the Bigfer2013 dataset, underscoring its significance in our research. In conclusion, our proposed model proves to be a viable solution for precise sentiment detection and estimation. In the future, our lightweight model may be deployed on embedded devices for research purposes.

A dual stream attention network for facial expression recognition in the wild

A dual stream attention network for facial expression recognition in the wild

Self-supervised extracted contrast network for facial expression recognition

Self-supervised extracted contrast network for facial expression recognition

An integrated attention-guided deep convolutional neural network for facial expression recognition in the wild

An integrated attention-guided deep convolutional neural network for facial expression recognition in the wild

Learning informative and discriminative semantic features for robust facial expression recognition

Facial expression recognition (FER) becomes challenging in real-world scenarios, which requires learning informative and discriminative features from challenging datasets to obtain robust facial expression recognition. In this paper, we propose an Informative and Discriminative Semantic Features Learning (IDSFL) network for FER against occlusion and head pose in the wild. Specifically, IDSFL aims to mine informative and discriminative semantic features from both low and high levels learned features to learn robust representations. First, a multi-channel feature (MCF) modulator incorporating low-level Gabor features is introduced to learn informative semantic features by capturing adequate diverse and detailed information. Additionally, a specific emotion-aware (SEA) module is proposed to learn discriminative semantic features by aggregating high-level emotion-specific features to focus on each expression category. Thus, IDSFL can collaboratively learn informative and discriminative representations. Extensive experiments on challenging in-the-wild datasets, including RAF-DB, FERPlus and AffectNet-7, demonstrate that our proposed method outperforms most state-of-the-art FER methods.

Hybrid Attention-Aware Learning Network for Facial Expression Recognition in the Wild

Hybrid Attention-Aware Learning Network for Facial Expression Recognition in the Wild

A discriminative multiscale feature extraction network for facial expression recognition in the wild

Driven by advancements in deep learning technologies, substantial progress has been achieved in the field of facial expression recognition over the past decade, while challenges remain brought about by occlusions, pose variations and subtle expression differences in unconstrained (wild) scenarios. Therefore, a novel multiscale feature extraction method is proposed in this paper, that leverages convolutional neural networks to simultaneously extract deep semantic features and shallow geometric features. Through the mechanism of channel-wise self-attention, prominent features are further extracted and compressed, preserving advantageous features for distinction and thereby reducing the impact of occlusions and pose variations on expression recognition. Meanwhile, inspired by the large cosine margin concept used in face recognition, a center cosine loss function is proposed to avoid the misclassification caused by the underlying interclass similarity and substantial intra-class feature variations in the task of expression recognition. This function is designed to enhance the classification performance of the network through making the distribution of samples within the same class more compact and that between different classes sparser. The proposed method is benchmarked against several advanced baseline models on three mainstream wild datasets and two datasets that present realistic occlusion and pose variation challenges. Accuracies of 89.63%, 61.82%, and 91.15% are achieved on RAF-DB, AffectNet and FERPlus, respectively, demonstrating the greater robustness and reliability of this method compared to the state-of-the-art alternatives in the real world.

Dual-branch Residual Disentangled Adversarial Learning Network for Facial Expression Recognition

Dual-branch Residual Disentangled Adversarial Learning Network for Facial Expression Recognition

Hierarchical Co-Consistency Quantization and Information Refining Binary Network for Facial Expression Recognition in Human–Robot Interaction

Hierarchical Co-Consistency Quantization and Information Refining Binary Network for Facial Expression Recognition in Human–Robot Interaction

Landmark-Based Adaptive Graph Convolutional Network for Facial Expression Recognition

Landmark-Based Adaptive Graph Convolutional Network for Facial Expression Recognition

A multi‐scale feature fusion convolutional neural network for facial expression recognition

AbstractThis paper designs a new facial Expression recognition network called a multi‐scale feature Fusion Convolutional neural Network (EFCN). This network is proposed to solve two problems in the facial expression recognition task. First, there are many commonalities between faces of different expression categories, and the recognition task cannot be precisely performed when the commonality is greater than the individuality. Secondly, facial detail features have a significant impact on the final results of expression recognition, while the image detail features extracted by traditional convolutional neural networks are not sufficient. In order to address the above issues, the feature enhancement network (FEN) and the detail information enhancement module (DEM) are designed. The FEN fuses deep and shallow features. Accordingly, the feature map contains richer information, making it easy to identify the samples. The DEM extracts and fuses the features passed by the backbone network with multi‐scale features to enhance the network's ability to extract features from small regions of the face. We validated the proposed method on three datasets, RAF‐DB, CK+, and JAFFE, and achieved 84.50%, 97.86%, and 91.05% accuracy, respectively, and the experimental results showed the effectiveness of the proposed method in this paper. For example, on the JAFFE dataset, the recognition accuracy of this method surpasses the MLT method by 1.87%.

Attention-Rectified and Texture-Enhanced Cross-Attention Transformer Feature Fusion Network for Facial Expression Recognition

Facial Expression Recognition (FER) in the wild is a challenging task for affective computing in human-machine interaction fields. However, most of existing methods fail to learn the most prominent regions of facial images by simple cross-entropy loss due to the imbalance problem commonly existing in FER datasets, which limits the robustness and interpretability of the model. In addition, these methods only capture local features of original images with multi-size shallow convolution and ignore facial texture characteristics, leading to a suboptimal recognition performance. To address these issues, we propose a novel facial expression recognition network, named the attention-rectified and texture-enhanced cross-attention transformer feature fusion network (AR-TE-CATFFNet). Specifically, an attention-rectified convolution block is first designed to assist multiple convolution heads to focus on critical areas of human faces and improve the model generalization. Second, we investigate a texture enhancement block to capture texture features through local binary pattern and gray-level co-occurrence matrix, which solves the limitation of insufficient texture information. Finally, a cross-attention transformer feature fusion block is employed to deeply integrate RGB features and texture features globally, which is beneficial to boost the accuracy of recognition. Competitive experimental results on three public datasets validate the efficacy of the proposed method, indicating that our proposed method achieves superior classification performance of 89.50% on RAF-DB dataset, 65.66% on AffectNet dataset, and 74.84% on FER2013 dataset against the existing methods. The code of our proposed method will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/smy17/AR-TE-CATFFNet</uri> .

Transformer embedded spectral-based graph network for facial expression recognition

Transformer embedded spectral-based graph network for facial expression recognition

Memristive patch attention neural network for facial expression recognition and edge computing.

Facial expression recognition has made a significant progress as a result of the advent of more and more convolutional neural networks (CNN). However, with the improvement of CNN, the models continues to get deeper and larger so as to a greater focus on the high-level features of the image and the low-level features tend to be lost. Because of the reason above, the dependence of low-level features between different areas of the face often cannot be summarized. In response to this problem, we propose a novel network based on the CNN model. To extract long-range dependencies of low-level features, multiple attention mechanisms has been introduced into the network. In this paper, the patch attention mechanism is designed to obtain the dependence between low-level features of facial expressions firstly. After fusion, the feature maps are input to the backbone network incorporating convolutional block attention module (CBAM) to enhance the feature extraction ability and improve the accuracy of facial expression recognition, and achieve competitive results on three datasets CK+(98.10%), JAFFE (95.12%) and FER2013 (73.50%). Further, according to the PA Net designed in this paper,a hardware friendly implementation scheme is designed based on memristor crossbars, which is expected to provide a software and hardware co-design scheme for edge computing of personal and wearable electronic products.

Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

The relationships among different subregions in facial images and their varying contributions to facial expression recognition indicate that using a fixed subregion weighting scheme would result in a substantial loss of valuable information. To address this issue, we propose a facial expression recognition network called BGA-Net, which combines bidirectional gated recurrent units (BiGRUs) with an attention mechanism. Firstly, a convolutional neural network (CNN) is employed to extract feature maps from facial images. Then, a sliding window cropping strategy is applied to divide the feature maps into multiple subregions. The BiGRUs are utilized to capture the dependencies among these subregions. Finally, an attention mechanism is employed to adaptively focus on the most discriminative regions. When evaluated on CK+, FER2013, and JAFFE datasets, our proposed method achieves promising results.

A Dual-Direction Attention Mixed Feature Network for Facial Expression Recognition

In recent years, facial expression recognition (FER) has garnered significant attention within the realm of computer vision research. This paper presents an innovative network called the Dual-Direction Attention Mixed Feature Network (DDAMFN) specifically designed for FER, boasting both robustness and lightweight characteristics. The network architecture comprises two primary components: the Mixed Feature Network (MFN) serving as the backbone, and the Dual-Direction Attention Network (DDAN) functioning as the head. To enhance the network’s capability in the MFN, resilient features are extracted by utilizing mixed-size kernels. Additionally, a new Dual-Direction Attention (DDA) head that generates attention maps in two orientations is proposed, enabling the model to capture long-range dependencies effectively. To further improve the accuracy, a novel attention loss mechanism for the DDAN is introduced with different heads focusing on distinct areas of the input. Experimental evaluations on several widely used public datasets, including AffectNet, RAF-DB, and FERPlus, demonstrate the superiority of the DDAMFN compared to other existing models, which establishes that the DDAMFN as the state-of-the-art model in the field of FER.

Analysis of Convolutional Neural Networks for Facial Expression Recognition on GPU, TPU and CPU

In light of the increasing computational capacity provided by Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Tensor Processing Units (TPUs), all of these were designed to speed up deep learning workloads, and the fact that this iteration of human-computer interaction is becoming more natural and social, it is clear that the field of human-computer interaction is poised for significant growth. The scientific community has found emotion recognition to be of tremendous interest and significance. Despite these advances, it is still desired that research into computational methods for identifying and recognizing emotions at the same ease as humans. This study uses Convolutional Neural Networks (CNN) for human emotion identification from facial expressions to delve deeper into this topic. The results demonstrated that training an Artificial Neural Networks (ANN) on GPUs might cut computational time by as much as 90% while accuracy could be raised up to 65%.