Smart touchless palm sensing via palm adjustment and dynamic registration
Dandan Fan, Xu Liang, Chunsheng Zhang, Junan Chen, Baoyuan Wu, Wei Jia, David D. Zhang · Nature Communications · 2025
Touchless palm recognition is increasingly popular for its effectiveness, privacy, and hygiene benefits in biometric systems. However, several challenges remain, including significant performance degradation caused by variations in palm positioning and capture distance. To address these issues, this paper introduces a comprehensive sensing system that integrates dynamic registration with robust palm adjustment. Specifically, we conduct a thorough investigation of distance variations to establish optimal registration settings. In addition, we propose an edge-aware, rotation-invariant region of interest alignment method, which ensures spatial alignment for any given palm across its different samples, even under challenging conditions. By embedding it into a palm registration framework based on video sequences, we improve the system’s ability to adapt to varying conditions automatically. Extensive experiments on various datasets demonstrate that the proposed method significantly enhances the performance of touchless palm recognition systems. Palm recognition faces challenges from variations in palm distance and pose. Here, the authors analyse the impact of distances, determine optimal registration settings, and introduce an edge-aware, rotation-invariant region of interest alignment method that ensures consistent palm region localization.