TY - GEN
T1 - Hand dorsal vein recognition based on deep hash network
AU - Zhong, Dexing
AU - Shao, Huikai
AU - Liu, Yu
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - As a unique biometric technology that has emerged in recent decades, hand dorsal vein recognition has received increasing attention due to its higher safety and convenience. In order to further improve the recognition accuracy, in this paper we propose an end-to-end method for recognizing Hand dorsal vein Based on Deep hash network (DHN), called HBD. The hand dorsal vein image is input into the simplified Convolutional Neural Networks-Fast (SCNN-F) to obtain convolution features. At the last fully connected layer, for the outputs of 128 neurons, sgn function is used to encode each image as 128-bit code. By comparing the distances between images after coding, it can be judged whether they are from the same person. Using a special loss function and training strategy, we verify the effectiveness of HBD on the NCUT, GPDS, and NCUT+GPDS database, respectively. The experimental results show that the HBD method can achieve comparable accuracy to the state-of-the-arts. In NCUT database, when the ratio of training and test set is 7:3, the Equal Error Rate (EER) of the test set is 0.08%, which is an order of magnitude lower than other algorithms. More importantly, due to the adoption of a simpler network structure and hash coding, HBD operates more efficiently and has superior performance gains over other deep learning methods while ensuring the accuracy.
AB - As a unique biometric technology that has emerged in recent decades, hand dorsal vein recognition has received increasing attention due to its higher safety and convenience. In order to further improve the recognition accuracy, in this paper we propose an end-to-end method for recognizing Hand dorsal vein Based on Deep hash network (DHN), called HBD. The hand dorsal vein image is input into the simplified Convolutional Neural Networks-Fast (SCNN-F) to obtain convolution features. At the last fully connected layer, for the outputs of 128 neurons, sgn function is used to encode each image as 128-bit code. By comparing the distances between images after coding, it can be judged whether they are from the same person. Using a special loss function and training strategy, we verify the effectiveness of HBD on the NCUT, GPDS, and NCUT+GPDS database, respectively. The experimental results show that the HBD method can achieve comparable accuracy to the state-of-the-arts. In NCUT database, when the ratio of training and test set is 7:3, the Equal Error Rate (EER) of the test set is 0.08%, which is an order of magnitude lower than other algorithms. More importantly, due to the adoption of a simpler network structure and hash coding, HBD operates more efficiently and has superior performance gains over other deep learning methods while ensuring the accuracy.
KW - Biometrics
KW - Deep hash network
KW - Hand dorsal vein recognition
UR - https://www.scopus.com/pages/publications/85057106806
U2 - 10.1007/978-3-030-03398-9_3
DO - 10.1007/978-3-030-03398-9_3
M3 - 会议稿件
AN - SCOPUS:85057106806
SN - 9783030033972
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 26
EP - 37
BT - Pattern Recognition and Computer Vision - First Chinese Conference, PRCV 2018, Proceedings
A2 - Lai, Jian-Huang
A2 - Zha, Hongbin
A2 - Zhou, Jie
A2 - Liu, Cheng-Lin
A2 - Tan, Tieniu
A2 - Zheng, Nanning
A2 - Chen, Xilin
PB - Springer Verlag
T2 - 1st Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018
Y2 - 23 November 2018 through 26 November 2018
ER -