Efficient iris texture analysis method based on Gabor ordinal measures

Imen Tajouri; Walid Aydi; Ahmed Ghorbel; Nouri Masmoudi

doi:10.1117/1.JEI.26.4.043012

28 July 2017 Efficient iris texture analysis method based on Gabor ordinal measures

Imen Tajouri, Walid Aydi, Ahmed Ghorbel, Nouri Masmoudi

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 4, 043012 (July 2017). https://doi.org/10.1117/1.JEI.26.4.043012

Abstract

With the remarkably increasing interest directed to the security dimension, the iris recognition process is considered to stand as one of the most versatile technique critically useful for the biometric identification and authentication process. This is mainly due to every individual’s unique iris texture. A modestly conceived efficient approach relevant to the feature extraction process is proposed. In the first place, iris zigzag “collarette” is extracted from the rest of the image by means of the circular Hough transform, as it includes the most significant regions lying in the iris texture. In the second place, the linear Hough transform is used for the eyelids’ detection purpose while the median filter is applied for the eyelashes’ removal. Then, a special technique combining the richness of Gabor features and the compactness of ordinal measures is implemented for the feature extraction process, so that a discriminative feature representation for every individual can be achieved. Subsequently, the modified Hamming distance is used for the matching process. Indeed, the advanced procedure turns out to be reliable, as compared to some of the state-of-the-art approaches, with a recognition rate of 99.98%, 98.12%, and 95.02% on CASIAV1.0, CASIAV3.0, and IIT Delhi V1 iris databases, respectively.

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Imen Tajouri, Walid Aydi, Ahmed Ghorbel, and Nouri Masmoudi "Efficient iris texture analysis method based on Gabor ordinal measures," Journal of Electronic Imaging 26(4), 043012 (28 July 2017). https://doi.org/10.1117/1.JEI.26.4.043012

Received: 3 March 2017; Accepted: 5 July 2017; Published: 28 July 2017

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