Share Email Print

Proceedings Paper

Novel algorithm for finger vein recognition based on inception-resnet module
Author(s): Xian Wang; Huabin Wang; Ying He; Yijun Ding; Liang Tao
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The finger vein feature extraction algorithm based on global or local features is sensitive to rotation, translation and scaling. Convolutional neural networks have higher robustness, but fewer finger vein samples are prone to over-fitting. Therefore, this paper designs a network architecture FingerveinNet for finger vein recognition. Firstly, based the Inception-resnet[1] module, the design of the finger vein network architecture is used to extract the multi-scale finger vein features while slowing down the gradient disappearance problem without increasing the parameters. Secondly, the center-loss is used as the loss function to optimize the network model and improve. The discriminability of feature vectors for better detail discrimination. Experiments on three international finger vein databases FV-TJ, FV-USM and PolyU show that the proposed method is robust to rotation and translation, and the effectiveness of the proposed method is verified.

Paper Details

Date Published: 14 August 2019
PDF: 9 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111791D (14 August 2019); doi: 10.1117/12.2539624
Show Author Affiliations
Xian Wang, Anhui Univ. (China)
Huabin Wang, Anhui Univ. (China)
Ying He, Anhui Univ. (China)
Yijun Ding, Anhui Univ. (China)
Liang Tao, Anhui Univ. (China)

Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?