A Novel Deep Learning Model for Palmprint/Palmvein Recognition

被引：2

作者：

Guo, Xiumei ^{[1
]}

Zhang, Ping ^{[1
]}

Wang, Chengyi ^{[1
]}

Sun, Bo ^{[1
]}

Sun, Saisai ^{[2
,3
]}

机构：

[1] Shandong Agr Univ, Coll Informat Sci & Engn, Tai An 271018, Shandong, Peoples R China

[2] Shandong First Med Univ, Coll Med Informat Engn, Tai An 271016, Shandong, Peoples R China

[3] Shandong Acad Med Sci, Tai An 271016, Shandong, Peoples R China

来源：

IEEE ACCESS | 2021年 / 9卷

基金：

中国国家自然科学基金;

关键词：

Feature extraction; Principal component analysis; Convolution; Classification algorithms; Training; Deep learning; Databases; PCANet; collaborative representation classifier; palmprint; palmvein recognition; PATTERN; PCANET;

D O I：

10.1109/ACCESS.2021.3110206

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this article, a novel palmprint/palmvein recognition algorithm is proposed. The algorithm firstly extracts the palmprint/palmvein feature by the principal component analysis network (PCANet), and then classifies by collaborative representation classifier (CRC). The proposed method is validated on several palmprint and palmvein databases, and the experimental results show that the method is very effective. At the same time, this method has good robustness for small training set. In the datasets of blue illuminations of Hong Kong Polytechnic University (PolyU) multispectral palmprint database, the recognition rate can reach 100% with only one training sample of each class. PCANet can extract palmprint depth feature information without the intervention of prior knowledge, and the CRC classifier can achieve high recognition accuracy rate while having an extremely low computational complexity. This algorithm can be used to real-time application.

引用

页码：122847 / 122854

页数：8

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