Cross-sensor iris matching using patch-based hybrid dictionary learning

Recently, more and more new iris acquisition devices appear on the market. In practical situation, it is highly possible that the iris images for training and testing are acquired by different iris image sensors. In that case, the recognition rate will decrease a lot and become much worse than the one when both sets of images are acquired by the same image sensors. Such issue is called "cross-sensor iris matching". In this paper, we propose a novel iris image hallucination method using a patch-based hybrid dictionary learning scheme which is able to hallucinate iris images across different sensors. Thus, given an iris image in test stage which is acquired by a new image sensor, a corresponding iris image will be hallucinated which looks as if it is captured by the old image sensor used in training stage. By matching training images with hallucinated images, the recognition rate can be enhanced. The experimental results show that the proposed method is better than the baseline, which proves the effectiveness of the proposed image hallucination method.