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access icon free Palmprint recognition using a modified competitive code with distinctive extended neighbourhood

© The Institution of Engineering and Technology
Received 22/12/2017, Accepted 25/06/2018, Revised 16/05/2018, Published 25/07/2018

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.

Inspec keywords: filtering theory; image fusion; image coding; image matching; palmprint recognition; feature extraction

Other keywords: local feature extraction; finding efficient matching strategies; robust orientation feature extraction; double-layer image pyramid; palmprint recognition; modified competitive code; distinctive extended eight-pixel neighbourhood method; public palmprint databases; filter responses; palmprint images; effective fusion matching scheme; similarity calculation

Subjects: Filtering methods in signal processing; Sensor fusion; Image and video coding; Computer vision and image processing techniques; Image recognition

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