Search

Under ideal image acquisition conditions, the iris biometric has been observed to provide high recognition performance compared to other biometric traits. Such a performance is possible by accurately segmenting the iris region from the given ocular image. This chapter discusses the challenges associated with the segmentation process, along with some of the prominent iris segmentation techniques proposed in the literature. The methods are presented according to their suitability for segmenting iris images acquired under different wavelengths of illumination. Furthermore, methods to refine and evaluate the output of the iris segmentation routine are presented. The goal of this chapter is to provide a brief overview of the progress made in iris segmentation.

This chapter discusses the performance of five different iris segmentation algorithms on challenging periocular images. The goal is to convey some of the difficulties in localizing the iris structure in images of the eye characterized by variations in illumination, eyelid and eyelash occlusion, de-focus blur, motion blur, and low resolution. The five algorithms considered in this regard are based on the (a) integro-differential operator; (b) hough transform; (c) geodesic active contours; (d) active contours without edges ; and (e) directional ray detection method. Experiments on the Face and Ocular Challenge Series (FOCS) Database highlight the pros and cons of the individual segmentation algorithms.

In this chapter, we describe the design and implementation of a distributed real-time face recognition system using a network of embedded cameras. We consider a scenario that simulates typical corridors and passages in airports and other indoor public spaces, where real-time human identification is of prime significance. We characterize system performance on an embedded camera network testbed which is assembled using commercial off-the-shelf components. We quantify the impact of multiple views on the accuracy of face recognition, and describe how distributed pre-processing and local filtering help in reducing both the network load, and the overall processing time.