Iris Segmentation in the Wild using Encoder-Decoder based Deep LearningTechniques

Shreshth Saini, Divij Gupta

Abstract:

Iris recognition is considered to be one of the most widely used biometric modality,
mainly due to its non-invasive nature and high reliability. However, in the whole process of
authentication, segmentation of the iris is the most crucial one as being the second stage
of the usual five-stage pipeline, the error introduced gets compounded in the subsequent
stages. However, segmentation of the iris in non-ideal conditions is a challenging task owing
to numerous artifacts such as occlusion by eyelids, off-angle rotations, irregular reflections,
blurred boundaries, etc. Although the artifacts can be minimized up to a certain extent
during the acquisition process, it requires a high level of control over the image capturing
environment and also high user cooperation, which is not always feasible. For segmentation,
quite a few methods have been put forward, but the ones using classical approaches usually
have low generalisability. Over the past decade, various deep learning techniques have been
proposed which have given satisfactory results. Since the problem at hand is that of an
image-to-image generation(the input image and its corresponding segmentation mask), the
most common similarity among them is the use of a standard encoder-decoder structure
called the UNet. In this chapter, we discuss several such techniques and their intricate novelties, and shortcomings, while also throwing some light on the non-deep learning methods
so as to get a wholesome comparison. We also discuss briefly about the various publicly
available datasets and the artifacts they are riddled with while also discussing about the
various metrics that are used by the scientific community to compare their works and establish the state-of-the-art. Lastly, we discuss a short implementation of the UNet done by
ourselves on two of the available datasets and conclude this chapter with a thought on the
future possibilities of the existing works