An efficient finger-vein extraction algorithm based on random forest regression with efficient local binary patterns
- Authors
- Liu, C.; Kim, Yeong-Hwa
- Issue Date
- Sep-2016
- Publisher
- IEEE Computer Society
- Keywords
- Finger-vein Extraction; Finger-vein Recognition; Local Binary Pattern; Random Forest Regression
- Citation
- Proceedings - International Conference on Image Processing, ICIP, v.2016-August, pp 3141 - 3145
- Pages
- 5
- Journal Title
- Proceedings - International Conference on Image Processing, ICIP
- Volume
- 2016-August
- Start Page
- 3141
- End Page
- 3145
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/52304
- DOI
- 10.1109/ICIP.2016.7532938
- ISSN
- 1522-4880
- Abstract
- Finger-vein, as a secure and convenient biometric characteristic in nature, has been widely studied for authentication in recent years. In this paper, we propose an efficient finger-vein extraction algorithm based on random forest training and regression with efficient local binary pattern feature. By integrating with a vein pattern matching method which is robust to finger misalignment, we achieved state-of-the-art finger-vein recognition. Thorough experiments have been conducted on two popular databases to prove the effectiveness and robustness of the proposed method. © 2016 IEEE.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Business & Economics > Department of Applied Statistics > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/52304)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.