Feature Extraction for Iris Recognition Based on Optimized Convolution Kernels

Abstract

Iris recognition has gained a lot of popularity for the last decades. Mainly a method based on binary iris templates found its way to real world use due to its simplicity, stability and reliability. The principle is that the unique iris structure is encoded to the bit code templates that are sufficient for high accuracy recognition. Encoding is performed by filtering a preprocessed iris image and storing only the phase information of the response to the filters. For years researchers used the 2D Gabor filters or their modifications, because these filters proved to provide the most reliable features. Despite the high recognition accuracy, the use of 2D Gabor filters faces a problem of spoofing. Recent studies show that the encoding process can be reverted and a spoofed iris can be obtained only based on the iris code. In this paper, we propose an efficient feature extraction method for iris recognition based on convolution kernels, learned from a database of irises. We show that the proposed method reaches state-of-the-art performance and can prohibit attackers from generating spoofed irises if the optimized convolution kernel is safely stored.