Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61527
Title: A 3D feature descriptor recovered from a single 2D palmprint image
Authors: Zheng, Q
Kumar, A 
Pan, G
Keywords: 3D feature from a single 2D image
Biometrics
Contactless palmprint matching
Ordinal features
Palmprint recognition
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on pattern analysis and machine intelligence, 2016, v. 38, no. 6, 7464743, p. 1272-1279 How to cite?
Journal: IEEE transactions on pattern analysis and machine intelligence 
Abstract: Design and development of efficient and accurate feature descriptors is critical for the success of many computer vision applications. This paper proposes a new feature descriptor, referred to as DoN, for the 2D palmprint matching. The descriptor is extracted for each point on the palmprint. It is based on the ordinal measure which partially describes the difference of the neighboring points' normal vectors. DoN has at least two advantages: 1) it describes the 3D information, which is expected to be highly stable under commonly occurring illumination variations during contactless imaging; 2) the size of DoN for each point is only one bit, which is computationally simple to extract, easy to match, and efficient to storage. We show that such 3D information can be extracted from a single 2D palmprint image. The analysis for the effectiveness of ordinal measure for palmprint matching is also provided. Four publicly available 2D palmprint databases are used to evaluate the effectiveness of DoN, both for identification and the verification. Our method on all these databases achieves the state-of-the-art performance.
URI: http://hdl.handle.net/10397/61527
ISSN: 0162-8828
EISSN: 1939-3539
DOI: 10.1109/TPAMI.2015.2509968
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