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. In recent years, in the field of artificial intelligence, deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance. Some researchers have tried to use convolutional neural networks (CNNs) for palmprint recognition and palm vein recognition. However, the architectures of these CNNs have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In order to overcome some shortcomings of manually designed CNN, neural architecture search (NAS) technology has become an important research direction of deep learning. The significance of NAS is to solve the deep learning model’s parameter adjustment problem, which is a cross-study combining optimization and machine learning. NAS technology represents the future development direction of deep learning. However, up to now, NAS technology has not been well studied for palmprint recognition and palm vein recognition. In this paper, in order to investigate the problem of NAS-based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct a performance evaluation of twenty representative NAS methods on five 2D palmprint databases, two palm vein databases, and one 3D palmprint database. Experimental results show that some NAS methods can achieve promising recognition results. Remarkably, among different evaluated NAS methods, ProxylessNAS achieves the best recognition performance.
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Acknowledgements
This work was supported by National Science Foundation of China (Nos. 62076086, 61673157, 61972129, 61972127 and 61702154), and Key Research and Development Program in Anhui Province (Nos. 202004d07020008 and 201904d07020010).
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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 Sciences, 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.
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. degree in automation from University of Science and Technology of China, China in 2008, and the Ph. D. degree in pattern recognition and intelligence system from University of Science and Technology of China, China in 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 the 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.
Yan-Xiang Chen received the B.Sc. and the M.Sc. degree in electronic information engineering from Hefei University of Technology, China in 1993 and 1996, and the Ph.D. degree in signal and information processing from University of Science and Technology of China, China in 2004. She has been a visiting scholar in University of Illinois at Urbana-Champaign, USA from 2006 to 2008, and in National University of Singapore, Singapore from 2012 to 2013. She is currently a professor in School of Computer Science and Information Engineering, Hefei University of Technology, China.
Her research interests include audio-visual signal processing, saliency and machine learning.
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Jia, W., Xia, W., Zhao, Y. et al. 2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search. Int. J. Autom. Comput. 18, 377–409 (2021). https://doi.org/10.1007/s11633-021-1292-1
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DOI: https://doi.org/10.1007/s11633-021-1292-1