Title: Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns
Authors: Yiding Wang; Qiangyu Duan; Lik-Kwan Shark; Di Huang
Addresses: School of Electronic and Information Engineering, North China University of Technology, Beijing 100144, China ' School of Electronic and Information Engineering, North China University of Technology, Beijing 100144, China ' Applied Digital Signal & Image Processing Research Centre, School of Engineering, University of Central Lancashire, Preston PR1 2HE, UK ' School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Abstract: Among biometric modalities, hand vein patterns are seen as providing an attractive method for high-level security access applications owing to high impenetrability and good user convenience. For biometric recognition based on near-infrared dorsal hand vein images, Local Binary Patterns (LBP) have emerged as a highly effective descriptor of local image texture with high recognition performance reported. In this paper, the traditional approach with LBP applied in the spatial domain is extended to multi-radius LBP in the wavelet domain to provide a more comprehensive set of feature categories to capture grey-level variation characteristics of vein patterns, and score weighted fusion based on the relative discriminative power of each feature category is proposed to achieve higher recognition performance. The proposed methodology is shown to provide a more robust performance with a recognition rate in excess of 99% and an equal error rate significantly less than 2%.
Keywords: biometrics; local binary patterns; score weighted fusion; hand vein recognition; wavelet transform; vein patterns; image texture; image recognition.
International Journal of Computer Applications in Technology, 2016 Vol.54 No.3, pp.151 - 160
Available online: 09 Oct 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article