Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/68931
Title: Door knob hand recognition system
Authors: Qu, X 
Zhang, D 
Lu, G
Guo, Z 
Keywords: Biometrics
Ergonomics
Feature extraction
Image processing
Machine learning
Optical imaging
Pattern recognition
User-centered design
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on systems, man, and cybernetics. Systems, 2016, v. PP, no. 99, p. 1-12 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics. Systems 
Abstract: 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.
URI: http://hdl.handle.net/10397/68931
ISSN: 2168-2216
EISSN: 2168-2232
DOI: 10.1109/TSMC.2016.2531675
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