Title: A finger vein recognition algorithm based on deep learning

Authors: Cheng Chen; Zhendong Wu; Jianwu Zhang; Ping Li; Freeha Azmat

Addresses: School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, China ' School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, China ' School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, China ' School of Mathematics and Computational Science, Sun Yatsen University, Guangzhou, China ' School of Engineering, University of Warwick Coventry, CV4 7AL, UK

Abstract: Deep learning has recently achieved impressive performance in the area of biometric recognition. The technology of finger vein recognition possesses better anti forgery performance and identification stability in collecting and certificating information of human bodies. The traditional finger vein recognition method is mainly based on template matching or whole feature recognition, suffering from light instability of the acquisition equipment which leads to low robustness. In this paper, we adapt a finger vein recognition algorithm using feature block fusion and deep belief network (FBF-DBN) and a convolutional neural network (CNN), then we combine these two network systems to judge the performance of our system. Meanwhile, we improve deep network input by using feature points set in vein images, effectively reducing the time in learning and detection, meeting the practical needs of biometric recognition specifically applied to embedded equipment. The experiment results showed that FBF-DBN and CNN algorithm present better recognition performance and faster speed.

Keywords: feature block fusion; FBF; deep belief network; DBN; convolution neural network; finger vein recognition.

DOI: 10.1504/IJES.2017.084690

International Journal of Embedded Systems, 2017 Vol.9 No.3, pp.220 - 228

Received: 01 Dec 2015
Accepted: 27 Mar 2016
Published online: 21 Jun 2017 *

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