Door Knob Hand Recognition System

Xiaofeng Qu, Dapeng Zhang, Guangming Lu, Zhenhua Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


Biometric applications have been used globally in everyday life. However, conventional biometrics is created and optimized for high-security scenarios. Being used in daily life by ordinary untrained people is a new challenge. Facing this challenge, designing a biometric system with prior constraints of ergonomics, we propose ergonomic biometrics design model, which attains the physiological factors, the psychological factors, and the conventional security characteristics. With this model, a novel hand-based biometric system, door knob hand recognition system (DKHRS), is proposed. DKHRS has the identical appearance of a conventional door knob, which is an optimum solution in both physiological factors and psychological factors. In this system, a hand image is captured by door knob imaging scheme, which is a tailored omnivision imaging structure and is optimized for this predetermined door knob appearance. Then features are extracted by local Gabor binary pattern histogram sequence method and classified by projective dictionary pair learning. In the experiment on a large data set including 12 000 images from 200 people, the proposed system achieves competitive recognition performance comparing with conventional biometrics like face and fingerprint recognition systems, with an equal error rate of 0.091%. This paper shows that a biometric system could be built with a reliable recognition performance under the ergonomic constraints.
Original languageEnglish
Article number7433472
Pages (from-to)2870-2881
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number11
Publication statusPublished - 1 Nov 2017


  • Biometrics
  • ergonomics
  • feature extraction
  • image processing
  • machine learning
  • optical imaging
  • pattern recognition
  • user-centered design

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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