Texture code matrix-based multi-instance iris recognition

This paper proposes a novel texture feature for iris recognition. The iris recognition system consists of three major components: pre-processing, feature extraction and classification. During pre-processing, iris is segmented using constrained circular Hough transform, which reduces both time and space complexity. In this work, from normalized iris image, a novel texture code matrix is generated, which is then used to obtain a co-occurrence matrix. Finally, desired texture features are computed from this co-occurrence matrix. Here, a two-class classification technique is adopted to develop a multi-class multimodal biometric system using fusion. The performance of the proposed system is tested on four standard iris image databases, namely UPOL, CASIA-Iris V3 Interval, MMU1 and IITD, which shows the efficacy of the proposed feature.