A Comparative Study of Palmprint Recognition Algorithms DAVID ZHANG, The Hong Kong Polytechnic University / Harbin Institute of Technology WANGMENG ZUO and FENG YUE, Harbin Institute of Technology Palmprint images contain rich unique features for reliable human identi cation, 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. Categories and Subject Descriptors: A.1 [Introductory and Survey]: I.4 [Image Processing and ; Computer Vision]:
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A Comparative Study of Palmprint Recognition Algorithms
Zhang, David; Zuo, Wangmeng; Yue, Feng
Follow ACM Computing Surveys (CSUR)
, Volume 44 (1)
Association for Computing Machinery – Jan 1, 2012
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