A Binary Code Learning Method Based on Class-neighborhood for Finger Vein Recognition

DOI

10.2991/icaita-18.2018.5How to use a DOI?

Keywords

finger vein recognition; binary code learning; class-neighborhood

Abstract

The finger vein recognition, as a new biometric identification technology, judges the identity of a person by using their finger vein angiography images obtained by near-infrared light, and has recently drawn considerable attention from scientists worldwide. In this work, a novel binary code learning method based on class-neighborhood (BCLCN) is proposed for finger vein recognition. Different from traditional binary code methods based on pixels, BCLCN fully considers the relationships between categories of train set and encodes based those relationships. In this way, the discrimination of the code is enhanced and the binary codes are shortened. Experimental results on self-built finger vein image database demonstrate the effectiveness of BCLCN (EER = 0.512%).

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Liping Zhang
AU  - Weijun Li
AU  - Xin Ning
AU  - Xiaoli Dong
AU  - Yakun Zhang
PY  - 2018/03
DA  - 2018/03
TI  - A Binary Code Learning Method Based on Class-neighborhood for Finger Vein Recognition
BT  - Proceedings of the 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018)
PB  - Atlantis Press
SP  - 16
EP  - 20
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-18.2018.5
DO  - 10.2991/icaita-18.2018.5
ID  - Zhang2018/03
ER  -