A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

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.

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Acknowledgements

This work was supported by National Science Foundation of China (Nos. 61673157, 62076086, 61972129 and 61702154), and Key Research and Development Program in Anhui Province (Nos. 202004d07020008 and 2019 04d07020010).

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Correspondence to Wei Jia.

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Wei Jia received the B.Sc. degree in informatics from Central China Normal University, China in 1998, the M. Sc. degree in computer science from Hefei University of Technology, China in 2004, and the Ph.D. degree in pattern recognition and intelligence system from University of Science and Technology of China, China in 2008. He has been a research associate professor in Hefei Institutes of Physical Science, Chinese Academy of Sciences, China from 2008 to 2016. He is currently an associate professor in Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, and in School of Computer Science and Information Engineering, Hefei University of Technology, China.

His research interests include computer vision, biometrics, pattern recognition, image processing and machine learning.

Jian Gao received the B.Sc. degree in mechanical design and manufacturing and automation from Hefei University of Technology, China in 2018. Now, he is currently a master student in School of Computer Science and Information Engineering, Hefei University of Technology, China.

His research interests include computer vision, biometrics recognition and deep learning. e]787117010@qq.com

Wei Xia received the B.Sc. degree in computer science from Anhui University of Science and Technology, China in 2018. He is a master student in School of Computer Science and Information Engineering, Hefei University of Technology, China.

His research interests include biometrics, pattern recognition and image processing.

Yang Zhao received the B.Eng. and Ph.D. degrees in pattern recognition and intelligence from Department of Automation, University of Science and Technology of China, China in 2008 and 2013. From 2013 to 2015, he was a postdoctoral researcher at School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, China. Currently, he is an associate professor at School of Computer Science and Information Engineering, Hefei University of Technology, China.

His research interests include image processing and computer vision.

Hai Min received the Ph.D. degree in pattern recognition and intelligence system from University of Science and Technology of China, China in 2014. He is currently an associate professor in School of Computer Science and Information Engineering, Hefei University of Technology, China.

His research interests include pattern recognition and image segmentation.

Jing-Ting Lu received the B.Sc, M.Sc. and Ph.D. degrees in computer science from Hefei University of Technology, China in 2004, 2009, and 2014, respectively. She is currently a lector in School of Computer and Information, Hefei University of Technology, China.

Her research interests include computer vision, biometrics, pattern recognition, image processing and machine learning.

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Jia, W., Gao, J., Xia, W. et al. A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition. Int. J. Autom. Comput. 18, 18–44 (2021). https://doi.org/10.1007/s11633-020-1257-9

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Keywords

  • Performance evaluation
  • convolutional neural network (CNN)
  • biometrics
  • palmprint
  • palm vein
  • deep learning