Enhancing gesture recognition with multiscale feature extraction and spatial attention - Jingpeng Lei (PLoS ONE, 2025)
Gesture recognition technology is a pivotal element in human-computer interaction, enabling users to communicate with machines in a natural and intuitive manner. This paper introduces a novel approach to gesture recognition that enhances accuracy and robustness by integrating multiscale feature extraction and spatial attention mechanisms. Specifically, we have developed a multiscale feature extraction module inspired by the Inception architecture, which captures comprehensive features across various scales, providing a more holistic feature representation. Additionally, We incorporate a spatial attention mechanism that focuses on image regions most relevant to the current gesture, thereby improving the discriminative power of the features. Extensive experiments conducted on multiple benchmark datasets demonstrate that our method significantly outperforms existing gesture recognition techniques in terms of accuracy.