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facial expression recognition in the wild: A new Adaptive Attention-Modulated Contextual Spatial Information network

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 124卷

作者： Li, Xue Zhu, Chunhua Yang, Shuzhi Henan Univ Technol Key Lab Grain Informat Proc & Control Minist Educ Zhengzhou 450001 Henan Peoples R China Henan Univ Technol Henan Engn Res Ctr Grain Condit Intelligent Detect Zhengzhou 450001 Henan Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Henan Peoples R China Henan Univ Technol Sch Artificial Intelligence & Big Data Zhengzhou 450001 Henan Peoples R China

facial expression recognition (FER) is an important and widely applied task. This paper proposes an Adaptive Attention-modulated Contextual Spatial Information (Ad-ACSI) model to improve FER in uncontrolled environments. The proposed Ad-ACSI model incorporates an Attention-modulated Contextual Spatial Information Network (ACSI-Net), a joint loss, and an adaptive attention modulator (AAM). The ACSI-Net, built on ResNet with contextual convolution (CoConv) and coordinated attention (CA), effectively captures global and local contextual features. The adaptive attention modulator (AAM) refines the features and generates dynamic weights for the center loss. The cross-entropy (CE) loss is refined into an equilibrium loss and combined with a sparse center loss to improve inter-class discrimination and intra-class clustering. Experiments on the RAF-DB and AffectNet datasets show that the proposed method achieves results comparable to state-of-the-art methods of FER in the wild, with it promising for integration into popular architectures.

关键词： facial expression recognition Contextual Spatial Information Joint loss Adaptive attention modulator

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facial expression recognition for Healthcare Monitoring Systems Using Neural Random Forest

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2025年第1期29卷 30-42页

作者： Siddiqi, Muhammad Hameed Ahmad, Irshad Alhwaiti, Yousef Khan, Faheem Jouf Univ Coll Comp & Informat Sci Sakaka 72388 Saudi Arabia Islamia Coll Dept Comp Sci Smart Technol Lab Peshawar 25000 Pakistan Gachon Univ Comp Sci Dept Seongnam 461701 South Korea

facial expressions vary with different health conditions, making a facial expression recognition (FER) system valuable within a healthcare framework. Achieving accurate recognition of facial expressions is a considerable challenge due to the difficulty in capturing subtle features. This research introduced an ensemble neural random forest method that utilizes convolutional neural network (CNN) architecture for feature extraction and optimized random forest for classification. For feature extraction, four convolutional layers with different numbers of filters and kernel sizes are used. Further, the maxpooling, batch normalization, and dropout layers are used in the model to expedite the process of feature extraction and avoid the overfitting of the model. The extracted features are provided to the optimized random forest for classification, which is based on the number of trees, criterion, maximum tree depth, maximum terminal nodes, minimum sample split, and maximum features per tree, and applied to the classification process. To demonstrate the significance of the proposed model, we conducted a thorough assessment of the proposed neural random forest through an extensive experiment encompassing six publicly available datasets. The remarkable weighted average recognition rate of 97.3% achieved across these diverse datasets highlights the effectiveness of our approach in the context of FER systems.

关键词： Feature extraction Random forests Face recognition Accuracy Medical services Robustness Monitoring Emotion recognition Classification tree analysis Support vector machines Classification decision tree facial expression recognition feature extraction healthcare neural network random forest

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facial expression recognition via joint loss constraining attention-modulated contextual spatial information network

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MULTIMEDIA SYSTEMS 2025年第1期31卷 1-12页

作者： Li, Xue Zhu, Chunhua Zhou, Fei Tao, Huawei Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Henan Peoples R China Henan Univ Technol Henan Key Lab Grain Photoelect Detect & Control Zhengzhou 450001 Henan Peoples R China Henan Univ Technol Key Lab Grain Informat Proc & Control Minist Educ Zhengzhou 450001 Henan Peoples R China Henan Univ Technol Sch Artificial Intelligence & Big Data Zhengzhou 450001 Henan Peoples R China

Convolutional neural networks (CNNs) are essentially applicable for feature extraction of images due to their robust mapping ability. However, due to the locality of CNNs, most of the existing methods that utilize CNNs for facial expression recognition (FER) cannot be well-suited for face images with occlusions or pose variations in the wild. Although our previous attention-modulated contextual spatial information network (ACSI-Net) is effective for FER, the loss function in ACSI-Net is not able to sufficiently constrain the network for the perception of discriminative facial features. In this paper, a novel constraining function called joint loss, tailored for FER in the wild, is offered to supervise ACSI-Net learning. In the Taylor expansion of the cross-entropy loss, the relative weights of the polynomial basis are adjusted to increase the learning intensity for categories with fewer samples, which produces our equilibrium loss. In addition, the regularization term is defined as the Euclidean distance between features and their class centers, with attention masking, which is named sparse center loss. Benefiting from the combination of equilibrium loss and sparse center loss, the joint loss can explore features with inter-class separateness and intra-class compactness from unbalanced data, leading to improved recognition performance. Experiments demonstrate that the presented joint loss, constraining ACSI-Net, achieves superior performance to ACSI-Net and is competitive with relevant advanced methods on two benchmark datasets, RAF_DB and AffectNet.

关键词： facial expression recognition Feature extraction Joint loss Inter-class separateness Intra-class compactness

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facial expression recognition based on local-global information reasoning and spatial distribution of landmark features

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VISUAL COMPUTER 2025年第1期41卷 535-548页

作者： Xiong, Kunhong Qing, Linbo Li, Lindong Guo, Li Peng, Yonghong Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China

In the field of facial expression recognition (FER), two main trends point to the data-driven FER and feature-driven FER exist. The former focused on the data problems (e.g., sample imbalance and multimodal fusion), while the latter explored the facial expression features. As the feature-driven FER is more important than the data-driven FER, for deeper mining of facial features, we propose an expression recognition model based on Local-Global information Reasoning and Landmark Spatial Distributions. Particularly to reason local-global information, multiple attention mechanisms with the modified residual module are designed for the Res18-LG module. In addition, taking the spatial topology of facial landmarks into account, a topological relationship graph of landmarks and a two-layer graph neural network are introduced to extract spatial distribution features. Finally, the experiment results on FERPlus and RAF-DB datasets demonstrate that our model outperforms the state-of-the-art methods.

关键词： facial expression recognition Local-global information Spatial distribution feature of landmarks Augmentation loss

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facial expression recognition for Probing Students' Emotional Engagement in Science Learning

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JOURNAL OF SCIENCE EDUCATION AND TECHNOLOGY 2025年第1期34卷 13-30页

作者： Tang, Xiaoyu Gong, Yayun Xiao, Yang Xiong, Jianwen Bao, Lei South China Normal Univ Sch Phys Guangzhou Peoples R China Ohio State Univ Dept Phys Columbus OH 43210 USA

Student engagement in science classroom is an essential element for delivering effective instruction. However, the popular method for measuring students' emotional learning engagement (ELE) relies on self-reporting, which has been criticized for possible bias and lacking fine-grained time solution needed to track the effects of short-term learning interactions. Recent research suggests that students' facial expressions may serve as an external representation of their emotions in learning. Accordingly, this study proposes a machine learning method to efficiently measure students' ELE in real classroom. Specifically, a facial expression recognition system based on a multiscale perception network (MP-FERS) was developed by combining the pleasure-displeasure, arousal-nonarousal, and dominance-submissiveness (PAD) emotion models. Data were collected from videos of six physics lessons with 108 students. Meanwhile, students' academic records and self-reported learning engagement were also collected. The results show that students' ELE measured by MP-FERS was a significant predictor of academic achievement and a better indicator of true learning status than self-reported ELE. Furthermore, MP-FERS can provide fine-grained time resolution on tracking the changes in students' ELE in response to different teaching environments such as teacher-centered or student-centered classroom activities. The results of this study demonstrate the validity and utility of MP-FERS in studying students' emotional learning engagement.

关键词： Student engagement Emotional learning engagement facial expression recognition Science teaching

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facial expression recognition Based on Feature Representation Learning and Clustering-Based Attention Mechanism

IEEE TRANSACTIONS ON BIOMETRICS, BEHAVIOR, AND IDENTITY SCIE...

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IEEE TRANSACTIONS ON BIOMETRICS, BEHAVIOR, AND IDENTITY SCIENCE 2025年第2期7卷 182-194页

作者： Jin, Lianghai Guo, Liyuan Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Peoples R China

facial expression recognition (FER) plays an important role in many computer vision applications. Generally, FER networks are trained based on the annotated labels or the probability distribution that an expression belongs to seven expression categories. However, the quality of annotations is heavily affected by ambiguous and indistinguishable facial expressions caused by compound and mixed emotions. Furthermore, it is difficult to annotate the seven-dimensional labels (probability distributions). To address these problems, this paper proposes a new FER network model. This model represents each type of facial expression as a high dimensional feature vector, based on which the FER network is trained. The high-dimensional feature representation of each facial expression class is learned by a special binary feature representation generator network. We also develop a clustering-based group split attention mechanism, which enhances the emotion-related features effectively. The experimental results on two lab-controlled datasets and four in-the-wild datasets demonstrate the effectiveness of the proposed FER model by showing clear performance improvements over other state-of-the-art FER methods. Codes are available at https://***/Gabrella/GLA-FNet.

关键词： Feature extraction Attention mechanisms Generators Emotion recognition Vectors Support vector machines Representation learning facial expression recognition attention mechanism feature representation

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facial expression recognition Method Based on Octonion Orthogonal Feature Extraction and Octonion Vision Transformer

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2025年第1期2025卷

作者： Tian, Yuan Cai, Hang Yao, Huang Chen, Di Cent China Normal Univ Fac Artificial Intelligence Educ Wuhan Peoples R China

In the field of artificial intelligence, facial expression recognition (FER) in natural scenes is a challenging topic. In recent years, vision transformer (ViT) models have been applied to FER tasks. The direct use of the original ViT structure consumes a lot of computational resources and longer training time. To overcome these problems, we propose a FER method based on octonion orthogonal feature extraction and octonion ViT. First, to reduce feature redundancy, we propose an orthogonal feature decomposition method to map the extracted features onto seven orthogonal sub-features. Then, an octonion orthogonal representation method is introduced to correlate the orthogonal features, maintain the intrinsic dependencies between different orthogonal features, and enhance the model's ability to extract features. Finally, an octonion ViT is presented, which reduces the number of parameters to one-eighth of ViT while improving the accuracy of FER. Experimental results on three commonly used facial expression datasets show that the proposed method outperforms several state-of-the-art models with a significant reduction in the number of parameters.

关键词： facial expression recognition octonion orthogonal feature vision transformer

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facial expression recognition with DToF Sensing

Facial Expression Recognition with DToF Sensing

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Chengxiao Zhang, Xie Wu, Chenshu Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

facial expression recognition (FER) is crucial for understanding human emotions, with applications spanning from mental health assessment to marketing recommendation systems. However, existing camera-based methods raise privacy concerns, while RF-based approaches suffer from limited environmental generalizability and high cost. In this work, we propose ToFace, a FER system leveraging a low-cost (4.8$) Direct Time-of-Flight (DToF) sensor that has been available on commodity smartphones. This sensor provides an extremely low-resolution 8 × 8 depth map and a clear Field of View (FoV), significantly mitigating privacy concerns while avoiding the impact of ambient objects. Despite the benefits, the low-resolution depth map introduces significant challenges for precise expression recognition due to limited facial structure information. We first develop a physical model to extract additional spatial information from the intermediate sensor output, i.e., the transient histograms. We then propose a physics-integrated neural network to reconstruct a facial structure map comprising both depth and orientation for accurate expression recognition. We conduct real-world experiments with 12 users and compare our model with several baselines. The results demonstrate that ToFace achieves the highest recognition accuracy of 75%. © 2025 IEEE.

关键词： direct time-of-flight sensor facial expression recognition internet of things sensing AI

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Structural self-contrast learning based on adaptive weighted negative samples for facial expression recognition

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VISUAL COMPUTER 2025年第1期41卷 579-590页

作者： Li, Huihui Zhu, Junhao Wen, Guihua Zhong, Haoyang Guangdong Polytech Normal Univ Sch Comp Sci Zhongshan Ave Guangzhou 510665 Guangdong Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

Face expression recognition in the wild faces challenges such as small data size, low quality images, and noisy labels. In order to solve these problems, this paper proposes a novel structural self-contrast learning method (SSCL) based on adaptively weighted negative samples. Firstly, two augmented variants are generated for each input sample as positive samples, which should be as similar in structure as possible. Secondly, EMD (Earth's Mover Distance) is used to define the structural similarity between the two augmented samples and extract the features in a self-supervised way. Subsequently, the concept of negative samples is further introduced into SSCL, and the augmented variants of other categories are generated as negative samples, which makes the structural similarity between positive samples increase and between negative samples simultaneously decrease. Finally, an adaptive weighting method for the negative is proposed, which aims to solve the imbalance between positive samples and negative samples, mine difficult negative samples, and reduce their similarity with the input image in an adaptive way. The extensive experiments on benchmark datasets demonstrate that SSCL outperforms the state-of-the-art methods. The cross-dataset evaluation shows the superior generalization capability of the proposed method. The code will be available at https://***/zjhzhy/SSCL.

关键词： facial expression recognition Deep learning Earth mover's distance Contrast learning

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Contrastive learning distillation of non-target categories for facial expression recognition

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年 1-13页

作者： An, Heng-Yu Jia, Rui-Sheng Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

facial expression recognition (FER) refers to the automated analysis of human emotional states using computer vision techniques, which are of significant importance in tasks such as fatigued driving detection, learning engagement analysis, and safety monitoring. In recent years, deep learning methods have made remarkable progress in facial expression recognition tasks. However, due to scarce samples, high intra-class variation, and inter-class similarity, current approaches perform poorly in recognizing difficult categories, such as Disgust and Fear. To alleviate the performance degradation resulting from these difficult categories, we propose a training framework called Contrastive Learning Distillation of Non-target Categories (CLDN). The proposed method consists of two stages: In the first stage, the model is jointly trained using contrastive learning and supervised learning to weaken the influence of imbalanced labels on visual representation learning, thereby generating robust soft labels. In the second stage, simple category knowledge is transferred to the difficult categories through self-distillation. During the distillation phase, a Non-target Categories Distillation Loss is introduced, alleviating the inhibition of classic distillation losses on non-target category knowledge and further promoting knowledge transfer during the distillation process. The proposed approach achieves competitive results on the RAF-DB, FERPlus, and AffectNet datasets. Ablation studies demonstrate that the method significantly improves the network's recognition accuracy for difficult categories. The code is available at https://***/Greysahy/CLDN.

关键词： facial expression recognition Contrastive learning Knowledge distillation

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