Palmprint Phenotype Feature Extraction and Classification Based on Deep Learning

Yu Fan; Jinxi Li; Shaoying Song; Haiguo Zhang; Sijia Wang; Guangtao Zhai

doi:10.1007/s43657-022-00063-0

Palmprint Phenotype Feature Extraction and Classification Based on Deep Learning

Phenomics. 2022 Jun 22;2(4):219-229. doi: 10.1007/s43657-022-00063-0. eCollection 2022 Aug.

Authors

Yu Fan^#¹, Jinxi Li^#^{2

3}, Shaoying Song⁴, Haiguo Zhang^{5

6}, Sijia Wang^{3

7}, Guangtao Zhai¹

Affiliations

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.

^# Contributed equally.

Abstract

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.

Keywords: Deep learning; Palmprint phenotype classification; Palmprint principal line extraction; ROI extraction.