Static and dynamic hand gesture recognition using deep learning - Liu, Yi (Nanyang Technological University, 2025)
This dissertation introduces an optimized hand gesture recognition (HGR) system to enhance human-computer interaction (HCI). By enabling natural and intuitive communication, the applications range from healthcare and rehabilitation to automotive systems and augmented reality. Despite recent advancements in the field, two key challenges persist: achieving reliable static gesture recognition under low-light conditions and developing efficient solutions for dynamic gesture recognition that do not rely on computationally heavy models. Existing approaches often suffer from degraded performance in poorly lit environments, while achieving high accuracy in dynamic gesture recognition typically requires large, resource-intensive models unsuitable for real-time or resource-constrained applications.