Iris is a physiological biometric trait, which is unique among all biometric traits to recognize person effectively. In this paper we propose Multi-scale Independent Component Analysis (ICA) based Iris Recognition using Binarized Statistical Image Features (BSIF) and Histogram of Gradient orientation (HOG). The Left and Right portion is extracted from eye images of CASIA V 1.0 database leaving top and bottom portion of iris. The multi-scale ICA filter sizes of 5X5, 7X7 and 17X17 are used to correlate with iris template to obtain BSIF. The HOGs are applied on BSIFs to extract initial features. The final feature is obtained by fusing three HOGs. The Euclidian Distance is used to compare the final feature of database image with test image final features to compute performance parameters. It is observed that the performance of the proposed method is better compared to existing methods
