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.