Xiaoliang Zhu, Hao Chen, Xin Yang, Zhicheng Dai, Liang Zhao^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1585-1602, 2025, DOI:10.32604/cmc.2025.066147 - 29 August 2025

Abstract Facial expression datasets commonly exhibit imbalances between various categories or between difficult and simple samples. This imbalance introduces bias into feature extraction within facial expression recognition (FER) models, which hinders the algorithm’s comprehension of emotional states and reduces the overall recognition accuracy. A novel FER model is introduced to address these issues. It integrates rebalancing mechanisms to regulate attention consistency and focus, offering enhanced efficacy. Our approach proposes the following improvements: (i) rebalancing weights are used to enhance the consistency between the heatmaps of an original face sample and its horizontally flipped counterpart; (ii) coefficient More >

Mengqi Li, Xiaodong Huang^*, Lifeng Wu

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 975-995, 2025, DOI:10.32604/cmc.2025.062621 - 09 June 2025

Abstract Micro-expressions, fleeting involuntary facial cues lasting under half a second, reveal genuine emotions and are valuable in clinical diagnosis and psychotherapy. Real-time recognition on resource-constrained embedded devices remains challenging, as current methods struggle to balance performance and efficiency. This study introduces a semi-lightweight multifunctional network that enhances real-time deployment and accuracy. Unlike prior simplistic feature fusion techniques, our novel multi-feature fusion strategy leverages temporal, spatial, and differential features to better capture dynamic changes. Enhanced by Residual Network (ResNet) architecture with channel and spatial attention mechanisms, the model improves feature representation while maintaining a lightweight design. More >

Adnan Ahmad, Zhao Li^*, Irfan Tariq, Zhengran He

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 729-749, 2025, DOI:10.32604/cmc.2024.055768 - 03 January 2025

Abstract Micro-expressions (ME) recognition is a complex task that requires advanced techniques to extract informative features from facial expressions. Numerous deep neural networks (DNNs) with convolutional structures have been proposed. However, unlike DNNs, shallow convolutional neural networks often outperform deeper models in mitigating overfitting, particularly with small datasets. Still, many of these methods rely on a single feature for recognition, resulting in an insufficient ability to extract highly effective features. To address this limitation, in this paper, an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm (IDSSCNN-XgBoost) is introduced for ME… More >

Yujian Wang¹, Jianxun Zhang^1,*, Renhao Sun²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 533-548, 2024, DOI:10.32604/cmc.2024.054644 - 15 October 2024

Abstract The effectiveness of facial expression recognition (FER) algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression data. However, labeling large datasets demands significant human, time, and financial resources. Although active learning methods have mitigated the dependency on extensive labeled data, a cold-start problem persists in small to medium-sized expression recognition datasets. This issue arises because the initial labeled data often fails to represent the full spectrum of facial expression characteristics. This paper introduces an active learning approach that integrates uncertainty estimation, aiming to improve the precision of facial… More >

Lin Wang¹, Juan Zhao², Hu Song³, Xiaolong Xu^4,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1105-1135, 2024, DOI:10.32604/cmc.2024.048688 - 25 April 2024

Abstract In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assess students’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis, thereby continuously promoting the improvement of teaching quality. However, most existing multi-face expression recognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance, and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single face images, which are of low quality and lack specificity, also restricting the development of this research. This paper aims to propose an end-to-end high-performance multi-face… More >

Zhanfeng Wang¹, Lisha Yao^2,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1659-1677, 2024, DOI:10.32604/cmc.2024.048304 - 25 April 2024

Abstract Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images, which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomes these limitations by vectorizing information through increased directionality and magnitude, ensuring that spatial information is not overlooked. Therefore, this study proposes a novel expression recognition technique called CAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining and integrating features extracted by a convolutional neural network before introducing them into CapsNet, our model enhances facial recognition capabilities. Compared to More >

Sherly Alphonse^*, Harshit Verma

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2067-2086, 2024, DOI:10.32604/cmc.2024.046070 - 27 February 2024

Abstract Although lots of research has been done in recognizing facial expressions, there is still a need to increase the accuracy of facial expression recognition, particularly under uncontrolled situations. The use of Local Directional Patterns (LDP), which has good characteristics for emotion detection has yielded encouraging results. An innovative end-to-end learnable High Response-based Local Directional Pattern (HR-LDP) network for facial emotion recognition is implemented by employing fixed convolutional filters in the proposed work. By combining learnable convolutional layers with fixed-parameter HR-LDP layers made up of eight Kirsch filters and derivable simulated gate functions, this network considerably More >

Chaoji Liu¹, Xingqiao Liu^1,*, Chong Chen², Kang Zhou¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 405-440, 2024, DOI:10.32604/cmes.2023.031040 - 30 December 2023

Abstract Pose-invariant facial expression recognition (FER) is an active but challenging research topic in computer vision. Especially with the involvement of diverse observation angles, FER makes the training parameter models inconsistent from one view to another. This study develops a deep global multiple-scale and local patches attention (GMS-LPA) dual-branch network for pose-invariant FER to weaken the influence of pose variation and self-occlusion on recognition accuracy. In this research, the designed GMS-LPA network contains four main parts, i.e., the feature extraction module, the global multiple-scale (GMS) module, the local patches attention (LPA) module, and the model-level fusion… More >

Wenqiu Zhu^1,2, Yongsheng Li^1,2, Qiang Liu^1,2,*, Zhigao Zeng^1,2

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1373-1392, 2023, DOI:10.32604/cmc.2023.037216 - 31 October 2023

Abstract Aiming at the problems of short duration, low intensity, and difficult detection of micro-expressions (MEs), the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature extraction. Based on traditional convolution neural network (CNN) and long short-term memory (LSTM), a recognition method combining global identification attention network (GIA), block identification attention network (BIA) and bi-directional long short-term memory (Bi-LSTM) is proposed. In the BIA, the ME video frame will be cropped, and the training will be carried out by cropping into 24 identification blocks (IBs), 10 IBs… More >

Amel Ali Alhussan¹, Fatma M. Talaat², El-Sayed M. El-kenawy³, Abdelaziz A. Abdelhamid^4,5, Abdelhameed Ibrahim⁶, Doaa Sami Khafaga^1,*, Mona Alnaggar⁷

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 499-515, 2023, DOI:10.32604/cmc.2023.039368 - 08 June 2023

Abstract In computer vision, emotion recognition using facial expression images is considered an important research issue. Deep learning advances in recent years have aided in attaining improved results in this issue. According to recent studies, multiple facial expressions may be included in facial photographs representing a particular type of emotion. It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition. The main contribution of this paper is to propose a facial expression recognition model (FERM) depending on an optimized Support Vector Machine (SVM). To test the… More >