标题:Combination of Fractional Brownian Random Field and Lacunarity for Iris Recognition
作者:Liu, Kai; Zhou, Weidong; Wang, Yu
通讯作者:Liu, K.
作者机构:[Liu, Kai; Zhou, Weidong; Wang, Yu] Shandong Univ, Sch Informat Sci & Engn, Jinan 250100, Peoples R China.
会议名称:International Conference on Graphic and Image Processing (ICGIP)
会议日期:OCT 01-02, 2011
来源:INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2011)
出版年:2011
卷:8285
DOI:10.1117/12.913474
关键词:iris recognition; feature extraction; Fractional Brownian Random Field;; lacunarity
摘要:Feature extraction plays a vital role in iris recognition, affecting the performance of iris recognition algorithm strongly. In this paper, we present an individual recognition algorithm using fractal dimension based on fractional Brownian random field and lacunarity in feature extraction. Making use of the fractal feature of iris, such as self-similarity and random patterns, fractal dimension can extract texture information effectively. Lacunarity overcomes the limitation of fractal dimension that fractal sets with different textures may share the same fractal dimension value. The combination of fractal dimension and lacunarity makes the feature extraction more comprehensive and distinguishable. The experimental results show that this recognition algorithm can obtain great performance on CASIA 1.0 iris database.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-80054693052&doi=10.1117%2f12.913474&partnerID=40&md5=54bb35d423f0bd2715938834a8656d57
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