Combination of Fractional Brownian Random Field and Lacunarity for Iris Recognition
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 selfsimilarity 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.
iris recognition feature extraction Fractional Brownian Random Field lacunarity
Kai Liu Weidong Zhou Yu Wang
School of Information Science and Engineering Shandong University Jinan, Chain
国际会议
2010 International Conference on Signal and Information Processing(2010年IEEE信号与信息处理国际会议 ICSIP2010)
长沙
英文
360-364
2010-12-14（万方平台首次上网日期，不代表论文的发表时间）