Affiliation:
1. The Hong Kong Polytechnic University / Harbin Institute of Technology
2. Harbin Institute of Technology
Abstract
Palmprint images contain rich unique features for reliable human identification, which makes it a very competitive topic in biometric research. A great many different low resolution palmprint recognition algorithms have been developed, which can be roughly grouped into three categories: holistic-based, feature-based, and hybrid methods. The purpose of this article is to provide an updated survey of palmprint recognition methods, and present a comparative study to evaluate the performance of the state-of-the-art palmprint recognition methods. Using the Hong Kong Polytechnic University (HKPU) palmprint database (version 2), we compare the recognition performance of a number of holistic-based (Fisherpalms and DCT+LDA) and local feature-based (competitive code, ordinal code, robust line orientation code, derivative of Gaussian code, and wide line detector) methods, and then investigate the error correlation and score-level fusion performance of different algorithms. After discussing the achievements and limitations of current palmprint recognition algorithms, we conclude with providing several potential research directions for the future.
Funder
National Natural Science Foundation of China
NSFC/SZHK funds
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Cited by
162 articles.
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