Abstract
Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.
Highlights
In the network and digital society, personal authentication is becoming a basic social service
This paper evaluates the performance of classic convolutional neural networks (CNNs) in 2D and 3D palmprint recognition and palm vein recognition
This paper systematically investigated the recognition performance of classic CNNs for 2D and 3D palmprint recognition and palm vein recognition
Summary
In the network and digital society, personal authentication is becoming a basic social service. Existing deep learning-based palmprint recognition and palm vein recognition work only used simple networks, and did not provide an in-depth analysis. It is very important to systematically investigate the recognition performance of classic CNNs for 2D and 3D palmprint recognition and palm vein recognition. In existing deep learning-based palmprint recognition and palm vein recognition methods, the training set often contains samples from both sessions. 3) We evaluated the performance of classic CNNs on Hefei University of Technology cross sensor palmprint database. To the best of our knowledge, it is the first time the problem of palmprint recognition across different devices using deep learning technology has been investigated.
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