- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
Emotion Recognition from Upper-Body Movements with a Pose Collage CNN Architecture | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Volume 25, Issue 2, October 2025, Pages 56-71 PDF (831.39 K) | ||
| Document Type: Research Paper | ||
| DOI: https://doi.org/10.33103/uot.ijccce.25.2.5 | ||
| Authors | ||
| Gheed Tawfeeq Waleed* 1; Shaimaa Hameed Shakir2 | ||
| 1phd student | ||
| 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. | ||
| Keywords | ||
| Body Movement; Deep learning; Emotion Recognition; Skelton; Convolution Neural Network | ||
|
Statistics Article View: 57 PDF Download: 48 |
||