Hand Vein Recognition with Single-layer Feature Learning Model
- 10.2991/icmmita-16.2016.257How to use a DOI?
- Vein Recognition; Single-layer; Feature Learning; K-Means; SVM
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.
- © 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/).
Cite this article
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 -