TY - GEN
T1 - End-to-End Generative Adversarial Network for Palm-Vein Recognition
AU - Qin, Huafeng
AU - El Yacoubi, Mounîm A.
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Palm-vein recognition has received increasing researchers’ attention in recent years. However, palm-vein recognition faces various challenges in practical applications, one of which is the lack of robustness against image quality degradation, resulting in reduction of the verification accuracy. To address this problem, this paper proposes an end-to-end convolutional neural network to automatically extract vein network features, thus without resorting to any hand-crafted features. Firstly, we label the palm-vein pixels based on several handcraft-based segmentation methods and reconstruct a training set accordingly. Secondly, an end-to-end vein segmentation model is proposed based on a generative adversarial network. After training, this model outputs a map where each value is the probability that the corresponding pixel belongs to a vein pattern. The resulting map is then subject to binarization by thresholding and stored in a binary image, used subsequently for verification matching. The experimental results on the public CASIA palm-vein dataset demonstrate the effectiveness of our proposed method.
AB - Palm-vein recognition has received increasing researchers’ attention in recent years. However, palm-vein recognition faces various challenges in practical applications, one of which is the lack of robustness against image quality degradation, resulting in reduction of the verification accuracy. To address this problem, this paper proposes an end-to-end convolutional neural network to automatically extract vein network features, thus without resorting to any hand-crafted features. Firstly, we label the palm-vein pixels based on several handcraft-based segmentation methods and reconstruct a training set accordingly. Secondly, an end-to-end vein segmentation model is proposed based on a generative adversarial network. After training, this model outputs a map where each value is the probability that the corresponding pixel belongs to a vein pattern. The resulting map is then subject to binarization by thresholding and stored in a binary image, used subsequently for verification matching. The experimental results on the public CASIA palm-vein dataset demonstrate the effectiveness of our proposed method.
KW - Convolutional neural network
KW - Generative adversarial network
KW - Palm-vein
KW - U-Net
U2 - 10.1007/978-3-030-59830-3_62
DO - 10.1007/978-3-030-59830-3_62
M3 - Conference contribution
AN - SCOPUS:85092927953
SN - 9783030598297
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 714
EP - 724
BT - Pattern Recognition and Artificial Intelligence - International Conference, ICPRAI 2020, Proceedings
A2 - Lu, Yue
A2 - Vincent, Nicole
A2 - Yuen, Pong Chi
A2 - Zheng, Wei-Shi
A2 - Cheriet, Farida
A2 - Suen, Ching Y.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020
Y2 - 19 October 2020 through 23 October 2020
ER -