Iris recognition in non-ideal imaging conditions

This paper studies the iris recognition problem in the degraded iris images captured in non-ideal imaging conditions. In these circumstances iris recognition becomes challenging because of noisy factors such as the off-axis imaging, pose variation, image blurring, illumination change, occlusion, specular highlights and noise. We introduce a robust algorithm based on the Random Sample Consensus (RANSAC) for localization of non-circular iris boundaries. It can localize the iris boundaries more accurately than the methods based on the Hough transform. To account for iris pattern deformation, we describe an image registration method based on the Lucas–Kanade algorithm. Operating on the filtered iris images, this method divides one image into small sub-images and solves registration problem for every small sub-image. Under some reasonable assumptions this method becomes very efficient while maintaining its effectiveness. Finally, we investigate how to extract highly distinctive features in the degraded iris images. We present a sequential forward selection method for seeking a sub-optimal subset of filters from a family of Gabor filters. The recognition performance is greatly improved with a very small number of filters selected. Experiments were conducted on the UBIRIS.v2 iris database and promising results were obtained.

► A RANSAC-based algorithm is introduced for localizing the iris boundaries.
► We investigate how to handle iris pattern deformation in recognition process.
► We present a sequential forward selection method for learning filters.