期刊
PATTERN RECOGNITION
卷 37, 期 6, 页码 1287-1298出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2004.02.001
关键词
iris recognition; local intensity variations; Gaussian-Hermite moments; Fisher linear discriminant; biometrics
As all emerging biometric for human identification, iris recognition has received increasing attention in recent years. This paper makes an attempt to reflect shape information of the iris by analyzing local intensity variations of an iris image. In Our framework, a set of one-dimensional (1D) intensity signals is constructed to contain the most important local variations of the original 2D iris image. Gaussian-Hermite moments of Such intensity signals reflect to a large extent their various spatial modes and are used as distinguishing features. A resulting high-dimensional feature vector is mapped into a low-dimensional subspace using Fisher linear discriminant, and then the nearest center classifier based on cosine similarity measure is adopted for classification. Extensive experimental results show that the proposed method is effective and encouraging. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
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