In this paper, we consider the problem of iris recognition in the context of video-based distant acquisition. We propose several systems aiming at improving the poor performance resulting from image degradations (low resolution, blur, and lack of texture) obtained from such acquisitions. Our approach is based on simple super-resolution techniques applied at the pixel level on the different frames of a video, improved by considering some quality criteria. Our main novelty is the introduction of a local quality measure in the fusion scheme. This measure relies on a gaussian mixture model estimation of clean iris texture distribution.
It can also be used to compute a global quality measure of the normalized iris image which can be used either for the selection of the best images in a sequence or in the fusion scheme. Extensive experiments on the QFIRE database at different acquisition distances (5, 7, and 11 ft) show the big improvement brought by the use of the global quality for both scenarios. Moreover, the local quality-based fusion scheme further increases the performance due to its ability to consider locally the different parts of the image, and therefore, to discard poorly segmented pixels in the fusion.