Abstract—Biometric systems provide automatic identification of the people base on their own characteristic features. Unlike the other biometric systems such as face, voice, vein, fingerprint recognitions, iris has randomly scattered features. Iris recognition is considered as the one of the most reliable and accurate biometric identification system. It consists of four stages such as; image acquisition, image preprocessing, image feature extraction, and image matching process. In this work, we are proposing a new feature extraction method and new matching metric in order to find an effective threshold value to separate the intra and inter class distribution of the iris images of different people by using eight – level quantization. Instead of using whole iris region, we have used statistically pre-selected iris regions. This selection had reduced the computational time and decreased the storage capacity. Our suggested metrics is rotation invariant and compares two vectors’ selected rows calculated by the Fourier Transform. We have suggested eight level quantization of the phase information in order to create iris feature extraction. Finally, we have shown ROC Curves to check the accuracy of our proposed metric. The accuracy of our work is 99.0%, proposed threshold value is 0.746 where FAR is 0.07%, FRR is 39.32% and AUC is 0.97. Using the CASIA Iris Database, we have compared our proposed matching metric with Hamming Distance metric and we report better performance in terms of matching time.
Index Terms—pattern recognition, iris recognition, feature vector extraction, biometrics, roc curves, quantization
Cite: Oktay Koç and Arban Uka, "A New Encoding of Iris Images Employing Eight Quantization Levels," Journal of Image and Graphics, Vol. 4, No. 2, pp. 78-83, December 2016. doi: 10.18178/joig.4.2.78-83
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