Ramakrishna Gandi^1,*, Geetha A.¹, Ramasubbareddy B.²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.077569 - 15 June 2026

Abstract In the fields of affective computing, human-computer interaction, and psychological evaluation, the capacity to recognize emotions is crucial. Unimodal systems in the form of visual systems or of the physiological type are usually not designed to capture the complexity that exists in emotional states. The paper proposes NeuroVision: Multimodal Emotion Recognition System, combining facial video frames information and electroencephalogram (EEG) based information to enhance the accuracy and stability of the system. The system applies ResNet50 on the spatial information of facial expressions, Vision Transformer (ViT) on the temporal movements in the video, and an EEG-MLP… More >

Mohammed A. Ahmed¹, Jian Dong^2,*, Ronghua Shi², Ammar Nassr³, Hani Almaqtari³, Ala A. Alsanabani³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075616 - 09 April 2026

Abstract Facial Expression Recognition (FER) is an essential endeavor in computer vision, applicable in human-computer interaction, emotion assessment, and mental health surveillance. Although Convolutional Neural Networks (CNNs) have proven effective in Facial Emotion Recognition, they encounter difficulties in capturing long-range connections and global context. To address these constraints, we propose Spatial Quad-Similarity Network (SQSNet), an innovative hybrid framework that integrates the local feature extraction capabilities of CNNs with the global contextual modeling efficacy of Swin Transformers via a cohesive fusion technique. SQSNet introduces the Spatial Quad-Similarity (SQS) module, a feature refinement approach that amplifies discriminative characteristics… More >

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 >

Anum Saher¹, Ghulam Gilanie^1,*, Sana Cheema¹, Akkasha Latif¹, Syeda Naila Batool¹, Hafeez Ullah²

Intelligent Automation & Soft Computing, Vol.39, No.6, pp. 1053-1071, 2024, DOI:10.32604/iasc.2024.057047 - 30 December 2024

Abstract Schizophrenia is a severe mental illness responsible for many of the world’s disabilities. It significantly impacts human society; thus, rapid, and efficient identification is required. This research aims to diagnose schizophrenia directly from a high-resolution camera, which can capture the subtle micro facial expressions that are difficult to spot with the help of the naked eye. In a clinical study by a team of experts at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan, there were 300 people with schizophrenia and 299 healthy subjects. Videos of these participants have been captured and converted into their frames using… More >

Shanjita Akter Prome¹, Md Rafiqul Islam^2,*, Md. Kowsar Hossain Sakib¹, David Asirvatham¹, Neethiahnanthan Ari Ragavan³, Cesar Sanin², Edward Szczerbicki⁴

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2845-2871, 2024, DOI:10.32604/cmc.2024.055311 - 18 November 2024

Abstract Deception detection is regarded as a concern for everyone in their daily lives and affects social interactions. The human face is a rich source of data that offers trustworthy markers of deception. The deception or lie detection systems are non-intrusive, cost-effective, and mobile by identifying facial expressions. Over the last decade, numerous studies have been conducted on deception detection using several advanced techniques. Researchers have focused their attention on inventing more effective and efficient solutions for the detection of deception. So, it could be challenging to spot trends, practical approaches, gaps, and chances for contribution.… 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 >

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 >

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 >

Hasan Han¹, Oguzcan Karadeniz¹, Tugba Dalyan^2,*, Elena Battini Sonmez², Baykal Sarioglu¹

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1209-1224, 2023, DOI:10.32604/iasc.2023.030674 - 29 April 2023

Abstract Robot companions will soon be part of our everyday life and students in the engineering faculty must be trained to design, build, and interact with them. The two affordable robots presented in this paper have been designed and constructed by two undergraduate students; one artificial agent is based on the Nvidia Jetson Nano development board and the other one on a remote computer system. Moreover, the robots have been refined with an empathetic system, to make them more user-friendly. Since automatic facial expression recognition skills is a necessary pre-processing step for acknowledging emotions, this paper More >