Optimal sampling for feature extraction in iris recognition systems

被引：0
|
作者：
Garza Castanon, Luis E. [1 ]
de Oca, Saul Montes [2 ]
Morales-Menendez, Ruben [3 ]
机构：
[1] Tecnnol Monterrey, Dept Mechatron & Automat, Campus Monterrey, Monterrey 489, NL, Mexico
[2] Tecnnol Monterrey, Automat Grad Program Student, Monterrey 489, NL, Mexico
[3] Tecnnol Monterrey, Ctr Innovat Design & Technol, Monterrey 489, NL, Mexico
来源：
MICAI 2006: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2006年 / 4293卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Iris recognition is a method used to identify people based on the analysis of the eye iris. A typical iris recognition system is composed of four phases: (1) image acquisition and preprocessing, (2) iris localization and extraction, (3) iris features characterization, and (4) comparison and matching. A novel contribution in the step of characterization of iris features is introduced by using a Hammersley's sampling algorithm and accumulated histograms. Histograms are computed with data coming from sampled sub-images of iris. The optimal number and dimensions of samples is obtained by the simulated annealing algorithm. For the last step, couples of accumulated histograms iris samples are compared and a decision of acceptance is taken based on an experimental threshold. We tested our ideas with UBIRIS database, for clean eye iris databases we got excellent results.
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页码：810 / +
页数：2
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