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ARNet: Enhanced Tracking and Optimization for Video-Based Affect Recognition
Othmani, Alice Ahlem Khan, Mustaqeem El Saddik, Abdulmotaleb Ei Moreira, Hugo Cogo
Othmani, Alice Ahlem
Khan, Mustaqeem
El Saddik, Abdulmotaleb Ei
Moreira, Hugo Cogo
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3749859.3749874.pdf
Adobe PDF, 1.64 MB
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Department
Computer Vision
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
Affect recognition plays a vital role in understanding individuals' emotional well-being and social interactions, particularly for children with autism who often face challenges in expressing their emotions. In this paper, we propose a novel approach to enhance affect recognition in children with autism by combining the power of deep learning with a new optimization method called "Stochastic Average Gradient Augmented with Tracking"(SAGAT). Through extensive experimentation, we demonstrate that the proposed approach significantly improves the accuracy of the Convolutional Neural Network (CNN) model compared to conventional optimization methods. Notably, our approach demonstrated strong accuracy in predicting arousal and valence in children with autism, with the CNN model achieving a mean squared error of 0.225 for arousal and 0.174 for valence on the SSBD-affect dataset. Pre-training on the AffectNet dataset further improved performance, reducing MSE to 0.187 for arousal and 0.156 for valence, highlighting the benefits of transfer learning. These findings hold significant implications for facilitating better understanding, support, and intervention strategies for children with autism, ultimately fostering their emotional well-being and social integration. This research opens up promising avenues for integrating advanced optimization techniques with deep learning to empower individuals with autism and promote inclusive technologies in affective computing.
Citation
A. Othmani, M. Khan, A. El Saddik, and H. C. Moreira, “ARNet: Enhanced Tracking and Optimization for Video-Based Affect Recognition,” IVSP 2025 - 2025 7th International Conference on Image, Video and Signal Processing, pp. 114–120, Sep. 2025, doi: 10.1145/3749859.3749874
Source
Proceedings of the 2025 7th International Conference on Image, Video and Signal Processing
Conference
7th International Conference on Image, Video and Signal Processing, IVSP 2025
Keywords
Affective Computing, Autism, Deep learning, Optimization, Video processing
Subjects
Source
7th International Conference on Image, Video and Signal Processing, IVSP 2025
Publisher
Association for Computing Machinery