Open AccessPalmprint recognition using a modified competitive code with distinctive extended neighbourhood
Author(s) -
Zhao Weiqiang,
Pang Liaojun,
Xiao Kai,
Wang Hua,
Cao Zhicheng,
Zhao Heng
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5306
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , coding (social sciences) , matching (statistics) , computer vision , feature extraction , pixel , code (set theory) , gabor filter , filter (signal processing) , orientation (vector space) , neighbourhood (mathematics) , similarity (geometry) , pyramid (geometry) , image (mathematics) , mathematics , mathematical analysis , statistics , geometry , set (abstract data type) , programming language
In recent years, palmprint recognition has made great progress and many methods have been put forward. The extraction of robust orientation features and finding efficient matching strategies are two key points for palmprint recognition. Traditional coding methods usually only use a dominant filter response to extract orientation features of palmprint images while not taking into account the other useful filter responses. Without increasing the number of filers, this study presents a modified Competitive Code to extract orientation features more accurately, which makes use of the relation between the filter responses. Besides, a distinctive extended eight‐pixel neighbourhood method is proposed to select the sample points for matching by extracting the local features. At the matching stage, an effective fusion matching scheme with a double‐layer image pyramid is designed to calculate the similarity between two palmprint images. Extensive experiments on four types of public palmprint databases show that the proposed method has excellent performance compared with the other state‐of‐the‐art algorithms.