Quality-Based Super Resolution for Degraded Iris Recognition

Abstract

In this paper we address the problem of low-quality iris recognition via super resolution approaches. We introduce two novel quality measures, one computed Globally (GQ) and the other Locally (LQ), for fusing at the pixel level (after a bilinear interpolation step) the images corresponding to several shots of a given person. These measures derive from a local GMM probabilistic characterization of good quality iris texture. We performed two types of experiments. The first one considers low resolution video sequences coming from the MBGC portal database: it shows the superiority of our approach compared to score-based or average image-based fusion methods. Moreover, we show that the LQ-based fusion outperforms the GQ-based fusion with a relative improvement of 4.79 % at the Equal Error Rate functioning point. The second experiment is performed on CASIA v4 database containing sequences of still images with degraded quality resulting in severe segmentation errors. We show that the image fusion scheme improves greatly the performance and that the LQ-based fusion is mainly interesting for low FAR values.

Deep learning based super-resolution for improved action recognition

“…This transformation is used to decompose the input image into structurally correlated subbands allowing exploiting the self-similarities between local neighbouring regions [1]. Existence of the-state-of-the-art techniques in super resolution has introduced a high impact into many image processing applications [17].…”