Musheng Chen^1,2, Qiang Wen¹, Xiaohong Qiu^1,2, Junhua Wu^1,*, Wenqing Fu¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071207 - 12 January 2026

Abstract In multi-modal emotion recognition, excessive reliance on historical context often impedes the detection of emotional shifts, while modality heterogeneity and unimodal noise limit recognition performance. Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions. To address these issues, we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition. A dynamic gating mechanism is applied across unimodal encoding, cross-modal alignment, and emotion transfer modeling, substantially improving noise robustness and feature alignment. First, we construct a unimodal encoder More >

Jun Li¹, Chunyan Liang^1,*, Zhiguo Liu¹, Fengpei Ge²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071011 - 12 January 2026

Abstract To enhance speech emotion recognition capability, this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup (AAM) and improved coordinate and shuffle attention (ICASA) methods. The AAM method optimizes data augmentation by combining a sample selection strategy and dynamic interpolation coefficients, thus enabling information fusion of speech data with different emotions at the acoustic level. The ICASA method enhances feature extraction capability through dynamic fusion of the improved coordinate attention (ICA) and shuffle attention (SA) techniques. The ICA technique reduces computational overhead by employing depth-separable convolution and an h-swish activation function and More >

Xiaorui Zhang^1,2,*, Chunlin Yuan³, Wei Sun⁴, Ting Wang⁵

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.071043 - 09 December 2025

Abstract Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression, but existing models have significant shortcomings. On the one hand, Transformer multi-head self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity. On the other hand, fixed-size convolution kernels are often used, which have weak perception ability for emotional regions of different scales. Therefore, this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling. In terms of space, multi-branch large-kernel stripe convolution is used to More >

A-Seong Moon¹, Seungyeon Jeong¹, Donghee Kim¹, Mohd Asyraf Zulkifley², Bong-Soo Sohn^3,*, Jaesung Lee^1,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2851-2872, 2025, DOI:10.32604/cmc.2025.067103 - 23 September 2025

Abstract Emotion recognition under uncontrolled and noisy environments presents persistent challenges in the design of emotionally responsive systems. The current study introduces an audio-visual recognition framework designed to address performance degradation caused by environmental interference, such as background noise, overlapping speech, and visual obstructions. The proposed framework employs a structured fusion approach, combining early-stage feature-level integration with decision-level coordination guided by temporal attention mechanisms. Audio data are transformed into mel-spectrogram representations, and visual data are represented as raw frame sequences. Spatial and temporal features are extracted through convolutional and transformer-based encoders, allowing the framework to capture… More > Graphic Abstract

Bowei Go, Xianli An^*

Journal of Psychology in Africa, Vol.35, No.4, pp. 523-533, 2025, DOI:10.32604/jpa.2025.070123 - 17 August 2025

Abstract This study investigated the impact of problematic mobile phone use (PMPU) on emotion recognition. The PMPU levels of 150 participants were measured using the standardized SAS-SV scale. Based on the SAS-SV cutoff scores, participants were divided into PMPU and Control groups. These participants completed two emotion recognition experiments involving facial emotion stimuli that had been manipulated to varying emotional intensities using Morph software. Experiment 1 (n = 75) assessed differences in facial emotion detection accuracy. Experiment 2 (n = 75), based on signal detection theory, examined differences in hit and false alarm rates across emotional expressions. More >

Selime Tuba Pesen, Mehmet Ali Altuncu^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5597-5611, 2025, DOI:10.32604/cmc.2025.066702 - 30 July 2025

Abstract EEG signals are widely used in emotion recognition due to their ability to reflect involuntary physiological responses. However, the high dimensionality of EEG signals and their continuous variability in the time-frequency plane make their analysis challenging. Therefore, advanced deep learning methods are needed to extract meaningful features and improve classification performance. This study proposes a hybrid model that integrates the Swin Transformer and Temporal Convolutional Network (TCN) mechanisms for EEG-based emotion recognition. EEG signals are first converted into scalogram images using Continuous Wavelet Transform (CWT), and classification is performed on these images. Swin Transformer is… More >

Yujiao Tang¹, Yadong Wu^1,*, Yuanmei He², Jilin Liu¹, Weihan Zhang¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2331-2352, 2025, DOI:10.32604/cmc.2024.059115 - 17 February 2025

Abstract Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion… More >

Xinlai Xing, Junliang Chen^*, Xiaochuan Zhang, Shuran Zhou, Runqing Zhang

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2275-2292, 2025, DOI:10.32604/cmc.2024.056877 - 17 February 2025

Abstract In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challenging task. However, previous work has primarily focused on the independent recognition of user intent and emotion, making it difficult to simultaneously track both aspects in the dialogue tracking module and to effectively utilize user emotions in subsequent dialogue strategies. We propose a Multi-Head Encoder Shared Model (MESM) that dynamically integrates features from emotion and intent encoders through a feature fusioner. Addressing the scarcity of datasets More >

Amjad Rehman¹, Muhammad Mujahid¹, Alex Elyassih¹, Bayan AlGhofaily¹, Saeed Ali Omer Bahaj^2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 41-72, 2025, DOI:10.32604/cmc.2024.058036 - 03 January 2025

Abstract In computer vision and artificial intelligence, automatic facial expression-based emotion identification of humans has become a popular research and industry problem. Recent demonstrations and applications in several fields, including computer games, smart homes, expression analysis, gesture recognition, surveillance films, depression therapy, patient monitoring, anxiety, and others, have brought attention to its significant academic and commercial importance. This study emphasizes research that has only employed facial images for face expression recognition (FER), because facial expressions are a basic way that people communicate meaning to each other. The immense achievement of deep learning has resulted in a… More >

Yuxiang Zou¹, Ning He^2,*, Jiwu Sun¹, Xunrui Huang¹, Wenhua Wang¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1255-1276, 2025, DOI:10.32604/cmc.2024.055732 - 03 January 2025

Abstract In recent years, gait-based emotion recognition has been widely applied in the field of computer vision. However, existing gait emotion recognition methods typically rely on complete human skeleton data, and their accuracy significantly declines when the data is occluded. To enhance the accuracy of gait emotion recognition under occlusion, this paper proposes a Multi-scale Suppression Graph Convolutional Network (MS-GCN). The MS-GCN consists of three main components: Joint Interpolation Module (JI Moudle), Multi-scale Temporal Convolution Network (MS-TCN), and Suppression Graph Convolutional Network (SGCN). The JI Module completes the spatially occluded skeletal joints using the (K-Nearest Neighbors)… More >