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Smart touchless palm sensing via palm adjustment and dynamic registration

Author

Listed:
  • Dandan Fan

    (The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen))

  • Xu Liang

    (Northwestern Polytechnical University)

  • Chunsheng Zhang

    (The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen))

  • Junan Chen

    (The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen))

  • Baoyuan Wu

    (The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen))

  • Wei Jia

    (Hefei University of Technology)

  • David Zhang

    (The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen))

Abstract

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.

Suggested Citation

  • Dandan Fan & Xu Liang & Chunsheng Zhang & Junan Chen & Baoyuan Wu & Wei Jia & David Zhang, 2025. "Smart touchless palm sensing via palm adjustment and dynamic registration," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58213-7
    DOI: 10.1038/s41467-025-58213-7
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    References listed on IDEAS

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    1. Zhongfang Zhang & Xiaolong Zhao & Xumeng Zhang & Xiaohu Hou & Xiaolan Ma & Shuangzhu Tang & Ying Zhang & Guangwei Xu & Qi Liu & Shibing Long, 2022. "In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
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