@inproceedings{0d09513c3fb34a58b740be9ca4f4078e,
title = "Comparative study of deep learning methods on dorsal hand vein recognition",
abstract = "In recent years, deep learning techniques have facilitated the results of many image classification and retrieval tasks. This paper investigates deep learning based methods on dorsal hand vein recognition and makes a comparative study of popular Convolutional Neural Network (CNN) architectures (i.e., AlexNet, VGG Net and GoogLeNet) for such an issue. To the best of our knowledge, it is the first attempt that applies deep models to dorsal hand vein recognition. The evaluation is conducted on the NCUT database, and state-of-the-art accuracies are reached. Meanwhile, the experimental results also demonstrate the advantage of deep features to the shallow ones to discriminate dorsal hand venous network and confirm the necessity of the fine-tuning phase.",
keywords = "Deep learning, Dorsal hand vein recognition, Fine-tuning",
author = "Xiaoxia Li and Di Huang and Yunhong Wang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 11th Chinese Conference on Biometric Recognition, CCBR 2016 ; Conference date: 14-10-2016 Through 16-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46654-5\_33",
language = "英语",
isbn = "9783319466538",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "296--306",
editor = "Shiguang Shan and Zhisheng You and Jie Zhou and Weishi Zheng and Yunhong Wang and Zhenan Sun and Jianjiang Feng and Qijun Zhao",
booktitle = "Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings",
address = "德国",
}