Hand Vein Recognition with Single-layer Feature Learning Model

DOI

10.2991/icmmita-16.2016.257How to use a DOI?

Keywords

Vein Recognition; Single-layer; Feature Learning; K-Means; SVM

Abstract

Performance of feature extraction and representation, sticking point of image recognition task, will directly influence the accuracy of final recognition. The traditional feature extraction algorithm of vein recognition is based on the sufficient prior knowledge of analysis on vein information characteristics, the shortcoming of which reflects in long time consumption spent on tuning parameters and special selection about later classifier to guarantee the final recognition rate as high as possible. The paper makes the attempt to introduce the K-means model, single-layer feature representation architecture, to the vein recognition task with some targeted modification, and adopts the SVM at the link of classifiers design. Finally, the proposed approach is rigorously evaluated on the self-built database and achieves the state-of-the-art RR (Recognition Rate) of 98.34%, which demonstrates the effectiveness of the proposed model.

Copyright

© 2017, 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  - Haoyu Wang
AU  - Xiaomin Liu
AU  - Bingguang Chen
PY  - 2017/01
DA  - 2017/01
TI  - Hand Vein Recognition with Single-layer Feature Learning Model
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 1090
EP  - 1094
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.257
DO  - 10.2991/icmmita-16.2016.257
ID  - Wang2017/01
ER  -