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valuable only if the initial low-resolution images are
blur-free and focused, stressing already the bad
influence of low quality images in the fusion. In
(Hollingsworth et al., 2009), authors proposed to
perform a simple averaging of the normalized iris
images extracted from the video for matching NIR
videos against NIR videos from the MBGC
database. When compared to a fusion of scores, the
results are similar but with a reduced complexity. In
the same spirit, Nguyen et al., (2010; 2011b)
proposed to fuse different images of the video at a
pixel level after an interpolation of the images. They
use a quality factor in their fusion scheme, which
allows giving less importance to images of bad
quality. The interpolation step is shown very
efficient as well as the quality weighting for
improving recognition performance. Note that they
considered a protocol similar to MBGC, where they
compare a video to a high quality still image. More
recent papers (Nguyen et al., 2011a); (Jillela et al.,
2011) explored the fusion in the feature domain
using PCA or PCT but not on the same MBGC
protocol as they usually degrade artificially the
image resolution in their assessment stage.
In our work, like in (Nguyen et al., 2011b), we
propose to fuse the different frames of the video at
the pixel level, after an interpolation stage which
allows increasing the size of the resulting image by a
factor of 2. Contrary to (Nguyen et al., 2011b), we
do not follow the MBGC protocol which compares a
video to a still high quality image reference but we
consider in our work, a video against video scenario,
more adapted to the re-identification context,
meaning that we will use several frames in both low
quality videos to address the person recognition
hypothesis.
The above literature review dealing with super
resolution in the iris on the move context has
stressed the importance of choosing adequately the
images involved in the fusion process. Indeed,
integration of low quality images leads to a decrease
in performance producing a rather counterproductive
effect.
In this work, we will therefore concentrate our
efforts in the proposition of a novel way of
measuring and integrating quality measures in the
image fusion scheme. More precisely our first
contribution is the proposition of a global quality
measure for normalized iris images as defined in
(Cremer et al., 2012) as a weighting factor in the
same way as proposed in (Nguyen et al., 2011b).
The interest of our measure compared to (Nguyen et
al., 2011b) is its simplicity and the fact that its
computation does not require to identify in advance
the type of degradations that can occur. Indeed our
measure is based on a local GMM-based
characterization of the iris texture. Bad quality
normalized iris images are therefore images
containing a large part of non-textured zones,
resulting from segmentation errors or blur.
Taking benefit of this local measure, we propose
as a second novel contribution to perform a local
weighting in the image fusion scheme, allowing this
way to take into account the fact that degradations
can be different in different parts of the iris image.
This means that regions free from occlusions will
contribute more in the reconstruction of the fused
image than regions with artifacts such as eyelid or
eyelash occlusion and specular reflection. Thus, the
quality of the reconstructed image will be better and
we expect this scheme to lead to a significant
improvement in the recognition performance.
This paper is organized as follows: in Section 2
we describe our approach for Local and Global
quality based super resolution and in Section 3 we
present the comparative experiments that we
performed on the MBGC database. Finally,
conclusions are given in Section 4.
2 LOCAL AND GLOBAL
QUALITY-BASED SUPER
RESOLUTION
In this Section, we will first briefly describe the
different modules of a video-based iris recognition
system. We will also recall the definition of the local
and global quality measure that we will use on the
normalized images. This concept has been described
in details in (Cremer et al., 2012); (Krichen et al.,
2007). We will explain how we have adapted this
measure to the context of iris images resulting from
low quality videos. We also describe the super-
resolution process allowing interpolation and fusion
of the frames of the video. Finally, we will
summarize the global architecture of the system that
we propose for person recognition from moving
person’s videos using these local and global quality
measures.
2.1 General Structure of Our
Video-based Iris Verification
System
For building an iris recognition system starting from
a video, several steps have to be performed. The first
need is the detection and tracking of the eyes in the
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