Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26195
Title: A comparative study of palmprint recognition algorithms
Authors: Zhang, D 
Zuo, W
Yue, F
Keywords: Biometrics
Feature extraction
Palmprint recognition
Performance evaluation
Person identification
Issue Date: 2012
Source: ACM Computing surveys, 2012, v. 44, no. 1, 2 How to cite?
Journal: ACM Computing Surveys 
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.
URI: http://hdl.handle.net/10397/26195
ISSN: 0360-0300
DOI: 10.1145/2071389.2071391
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