Waleed, G., Shakir, S. (2025). Emotion Recognition from Upper-Body Movements with a Pose Collage CNN Architecture. , 25(2), 56-71. doi: https://doi.org/10.33103/uot.ijccce.25.2.5
Gheed Tawfeeq Waleed; Shaimaa Hameed Shakir. "Emotion Recognition from Upper-Body Movements with a Pose Collage CNN Architecture". , 25, 2, 2025, 56-71. doi: https://doi.org/10.33103/uot.ijccce.25.2.5
Waleed, G., Shakir, S. (2025). 'Emotion Recognition from Upper-Body Movements with a Pose Collage CNN Architecture', , 25(2), pp. 56-71. doi: https://doi.org/10.33103/uot.ijccce.25.2.5
Waleed, G., Shakir, S. Emotion Recognition from Upper-Body Movements with a Pose Collage CNN Architecture. , 2025; 25(2): 56-71. doi: https://doi.org/10.33103/uot.ijccce.25.2.5
Emotion Recognition from Upper-Body Movements with a Pose Collage CNN Architecture
IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING
2Computer science department, University of Technology , Baghdad, Iraq
Abstract
Emotion recognition through body movement presents a compelling alternative to traditional facial and vocal analysis in the field of emotion recognition . This study introduces a deep learning-based framework that utilizes upper-body movements features to classify emotional states using Convolutional Neural Networks (CNNs). The system is trained and evaluated on the Body Language Dataset (BoLD) dataset, targeting seven primary emotions: happiness, sadness, anger, fear, surprise, joy, and disgust. The proposed model achieves a classification accuracy of 95.72%, significantly outperforming existing methods in body- based emotion recognition. The result highlights the potential of body posture as a reliable and standalone modality for emotion detection, especially in contexts where facial information is ambiguous or unavailable. The presented work contributes to the advancement of non-intrusive, vision-based affective computing systems, with promising applications in human-computer interaction, behavioral analysis, security, and assistive technologies. Future research may explore multimodal integration, real-time deployment, and cross-cultural adaptability to further enhance the robustness and versatility of body-driven emotion recognition systems.