Iris recognition using local texture analysis

Jen-Chun Lee; Ping Sheng Huang; Jyh-Chian Chang; Chien-Ping Chang; Te-Ming Tu

doi:10.1117/1.2948407

1 June 2008 Iris recognition using local texture analysis

Jen-Chun Lee, Ping Sheng Huang, Jyh-Chian Chang, Chien-Ping Chang, Te-Ming Tu

Author Affiliations +

Optical Engineering, 47(6), 067205 (2008). https://doi.org/10.1117/1.2948407

Abstract

With the increasing needs in security systems, iris recognition is reliable as one of the important solutions for biometrics-based identification systems. This work presents an effective approach for iris recognition by analyzing iris patterns. To improve the rate of recognition, we divide the normalized iris image into several regions to keep the iris image away from several noise factors, such as eyelids, eyelashes, and motion blur. For feature extraction, the local edge pattern (LEP) operator is designed to capture local characteristics of the iris image to produce discriminating texture features in every region. A resulting 2D feature vector is mapped into a low-dimensional subspace using two dimension linear discriminant analysis (2DLDA), and then the minimum distance classifier (MDC) is adopted for recognition. Experiments on the public and freely available iris images taken from the CASIA (Institute of Automation, Chinese Academy of Sciences) and UBIRIS databases confirm the advantage of the proposed approach in terms of speed and accuracy.

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Jen-Chun Lee, Jen-Chun Lee, Ping Sheng Huang, Ping Sheng Huang, Jyh-Chian Chang, Jyh-Chian Chang, Chien-Ping Chang, Chien-Ping Chang, Te-Ming Tu, Te-Ming Tu, } "Iris recognition using local texture analysis," Optical Engineering 47(6), 067205 (1 June 2008). https://doi.org/10.1117/1.2948407 . Submission:

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