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Dual iris matching for biometric identification

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Abstract

In this paper, a dual iris-based biometric identification system that increases the accuracy and the performance of a typical human iris recognition system is proposed. This system detects, isolates, and extracts the iris region from the eye images and maps the circular regions into rectangular polar coordinates according to the preset radial and angular resolutions. The phase responses, obtained from convolving the polar images with 1D log Gabor filter, are quantized to generate the binary iris templates which are compared using the hamming distance. Unlike the conventional method which examines a single eye, the proposed method takes images from both eyes simultaneously. Using it on CASIA Iris database V3, false positive rate and false negative rate were found to be 0 and 9.96 %, respectively, while the overall accuracy was 99.92 %.

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Abbreviations

\(\theta \) :

Spatial frequency for an iris image

\(\phi \) :

Orientation of an image at point \((x, y)\) on the image plane

g(x, y, \(\theta ,\,\phi )\) :

1D Gabor filter as a function of x, y, \(\theta ,\,\phi \)

\(f_{o }\) :

Central frequency of 1D log Gabor filter.

\(\sigma \) :

Bandwidth of 1D log Gabor filter

\(G(f) \) :

Frequency response of 1D log Gabor filter

Xj and Yj :

Two bitwise iris templates

\(N \) :

Number of bits in each iris template

Xnj andYnj :

The corresponding noise mask of two iris templates Xj and Yj

\(\oplus \) :

XOR operator

\(\wedge \) :

AND operator

\(\vee \) :

OR operator

HD :

Hamming distance between two iris template

\(\mu _{s }\) :

Mean of intra-class hamming distance

\(\mu _{d }\) :

Mean of inter-class hamming distance

\(\sigma _{s }\) :

Standard deviation of intra-class hamming distance

\(\sigma _{d }\) :

Standard deviation of inter-class hamming distance

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Acknowledgments

This Research is supported by G4S Bangladesh (www.g4s.com.bd). The authors would like to thank the Chinese Academy of Sciences’ Institute of Automation (CASIA) for making the iris database public.

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Authors and Affiliations

  1. Computer Vision and Cybernetics Research Group, School of Engineering and Computer Science, Independent University Bangladesh, Dhaka, Bangladesh

    K. M. Iftakhar Hasan & M. Ashraful Amin

  2. Department of Electrical Engineering and Computer Science, North South University, Dhaka, Bangladesh

    K. M. Iftakhar Hasan

  3. Department of Electronic Engineering, City University of Hong Kong, Hong Kong, SAR

    M. Ashraful Amin

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  1. K. M. Iftakhar Hasan
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Correspondence to M. Ashraful Amin.

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Hasan, K.M.I., Amin, M.A. Dual iris matching for biometric identification. SIViP 8, 1605–1611 (2014). https://doi.org/10.1007/s11760-012-0399-9

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