Iris feature extraction based on steerable pyramid representation

In this paper we propose an efficient local appearance feature extraction method for iris recognition based on steerable pyramid (S-P) to captures the intrinsic geometrical structures of iris image. It decomposes the iris image into a set of directional sub-bands with texture details captured in different orientations at various scales, local information is extracted from S-P sub-bands using block-based statistics to reduce the required amount of data to be stored. The obtained local features are combined at the feature and decision level to enhance iris recognition performance. Experimental evaluation using the CASIA iris image database (version 1) clearly demonstrates an efficient performance of the proposed algorithm.