A Lightweight Dynamic Gesture Recognition Model Driven by Meta-Learning Under Small Sample Conditions

Dynamic gesture recognition under small-sample conditions remains challenging due to the large variations caused by users, viewpoints, motion patterns, and hand configurations. This study proposes a lightweight dynamic gesture recognition framework that integrates meta-learning, Neural Architecture Search (NAS), and knowledge distillation (KD) to achieve rapid adaptation with limited training samples. Different from conventional recognition methods that mainly focus on feature extraction and model optimization, this study further considers the intrinsic symmetry characteristics of hand gestures. A mirror-aware skeleton representation strategy is introduced by modeling the structural correspondence between left- and right-hand keypoints, which reduces the distribution differences caused by hand-side variations and improves the generalization ability under few-shot conditions. The proposed framework adopts a lightweight spatio-temporal feature extraction module, an optimization-based meta-learning strategy, and a knowledge distillation mechanism to balance recognition accuracy and computational efficiency. Experiments are conducted on DHG-14, SHREC2017, FPHA, LMDHG, and 20BN-Jester datasets under different few-shot settings, including cross-user variation, viewpoint variation, motion speed variation, partial occlusion, and background interference. The experimental results demonstrate that the proposed method achieves competitive recognition performance while significantly reducing model complexity and computational cost. The proposed framework provides an efficient solution for dynamic gesture recognition in resource-constrained scenarios.