Palmprint recognition using Gabor-based local invariant features

Variations occurred on palmprint images degrade the performance of recognition. In this paper, we propose a novel approach to extract local invariant features using Gabor function, to handle the variations of rotation, translation and illumination, raised by the capturing device and the palm structu...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 72; no. 7; pp. 2040 - 2045
Main Authors Pan, Xin, Ruan, Qiu-Qi
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.03.2009
Subjects
Feature extraction
Gabor function
Invariant
Palmprint recognition
Invariant
Feature extraction
Gabor function
Palmprint recognition
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Summary:Variations occurred on palmprint images degrade the performance of recognition. In this paper, we propose a novel approach to extract local invariant features using Gabor function, to handle the variations of rotation, translation and illumination, raised by the capturing device and the palm structure. The local invariant features can be obtained by dividing a Gabor filtered image into two-layered partitions and then calculating the differences of variance between each lower-layer sub-block and its resided upper-layer block (called local relative variance). The extracted features only reflect relations between local sub-blocks and its resided upper-layer block, so that the global disturbance occurred on palmprint images is counteracted. The effectiveness of the proposed method is demonstrated by the experimental results.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2008.11.019