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 method...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.展开更多
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.).
文摘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.
