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1. IMPACT OF QUALITY-BASED FUSION TECHNIQUES FOR VIDEO-BASED IRIS
RECOGNITION AT A DISTANCE
ABSTRACT
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, 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 taking into account 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 feet) 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.