Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32409
Title: HMMs based palmprint identification
Authors: Wu, X
Wang, K
Zhang, D 
Issue Date: 2004
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2004, v. 3072, p. 775-781
Abstract: This paper presents a novel approach of palmprint identification with Hidden Markov Models (HMMs). Palmprint is first aligned and normalized by using the boundary of the fingers. Then the continuous HMMs are used to identify palmprints. The palmprint features are extracted by using Sobel operators and projecting technique. It shows that HMMs with six states and two Gaussian mixtures can obtain the highest identification rate, 97.80%, in one-to-320 matching test. Experimental results demonstrate the feasibility of HMMs on the palmprint identification task.
Publisher: Springer
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
ISSN: 0302-9743
EISSN: 1611-3349
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