2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search  

作　　者：Wei Jia Wei Xia Yang Zhao Hai Min Yan-Xiang Chen 

机构地区：[1]School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230009,China [2]Anhui Province Key Laboratory of Industry Safety and Emergency Technology,Hefei 230009,China

出　　处：《International Journal of Automation and computing》2021年第3期377-409,共33页国际自动化与计算杂志（英文版）

基　　金：supported by National Science Foundation of China(Nos.62076086,61673157,61972129,61972127 and 61702154);Key Research and Development Program in Anhui Province(Nos.202004d07020008 and 201904d07020010)。

摘　　要：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,Proxyless NAS achieves the best recognition performance.

关 键 词：Performance evaluation neural architecture search BIOMETRICS PALMPRINT palm vein deep learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]