Palmprint Phenotype Feature Extraction and Classification Based on Deep Learning  

作　　者：Fan Jinxi Li Shaoying Song Haiguo Zhang Sijia Wang Guangtao Zhai 

机构地区：[1]School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,People’s Republic of China [2]State Key Laboratory of Genetic Engineering,Human Phenome Institute,Zhangjiang Fudan International Innovation Center,Fudan University,Shanghai 201203,People’s Republic of China [3]CAS Key Laboratory of Computational Biology,Shanghai Institute of Nutrition and Health,University of Chinese Academy of Sciences,Chinese Academy of Sciences,Shanghai 200031,People’s Republic of China [4]BGI College and Henan Institute of Medical and Pharmaceutical Sciences,Zhengzhou University,Zhengzhou 450052,People’s Republic of China [5]Ministry of Education Key Laboratory of Contemporary Anthropology,Department of Anthropology and Human Genetics,School of Life Sciences,Fudan University,Shanghai 200438,People’s Republic of China [6]School of Basic Medicine,Shanghai Jiao Tong University School of Medicine,Shanghai Jiao Tong University,Shanghai 200025,People’s Republic of China [7]Center for Excellence in Animal Evolution and Genetics,Chinese Academy of Sciences,Kunming 650223,People’s Republic of China

出　　处：《Phenomics》2022年第4期219-229,共11页表型组学（英文）

基　　金：We would like to thank the participants of the CAS_palm set who consented to participate in research.This project was funded by the Shanghai Municipal Science and Technology Major Project 2017SHZDZX01(S.W.);National Natural Science Foundation of China Grant 61831015(G.Z.);China Postdoctoral Science Foundation Grant 2019M651351(J.L.).

摘　　要：Palmprints are of long practical and cultural interest.Palmprint principal lines,also called primary palmar lines,are one of the most dominant palmprint features and do not change over the lifespan.The existing methods utilize filters and edge detection operators to get the principal lines from the palm region of interest(ROI),but can not distinguish the principal lines from fine wrinkles.This paper proposes a novel deep-learning architecture to extract palmprint principal lines,which could greatly reduce the influence of fine wrinkles,and classify palmprint phenotypes further from 2D palmprint images.This architecture includes three modules,ROI extraction module(REM)using pre-trained hand key point location model,principal line extraction module(PLEM)using deep edge detection model,and phenotype classifier(PC)based on ResNet34 network.Compared with the current ROI extraction method,our extraction is competitive with a success rate of 95.2%.For principal line extraction,the similarity score between our extracted lines and ground truth palmprint lines achieves 0.813.And the proposed architecture achieves a phenotype classification accuracy of 95.7%based on our self-built palmprint dataset CAS_Palm.

关 键 词：Palmprint principal line extraction Palmprint phenotype classification ROI extraction Deep learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]