Heterogeneous IRIS recognition using heterogeneous eigeniris and sparse representation

When the iris images for training and testing are acquired by different iris image sensors, the recognition rate will be degraded and not as good as the one when both sets of images are acquired by the same image sensors. Such problem is called 'heterogeneous iris recognition'. In this paper, we propose two novel patch-based heterogeneous dictionary learning methods using heterogeneous eigeniris and sparse representation which learn the basic atoms in iris textures across different image sensors and build connections between them. After such connections are built, at testing stage, it is possible to hallucinate (synthesize) iris images across different sensors. By matching training images with hallucinated images, the recognition rate can be successfully enhanced. Experimenting with an iris database consisting of 3015 images, we show that the EER is decreased 23.9% relatively by the proposed method using sparse representation, which proves the effectiveness of the proposed image hallucination method.