Sclera recognition: on the quality measure and segmentation of degraded images captured under relaxed imaging conditions

Abstract

The authors propose a new method for sclera quality measure and segmentation under relaxed imaging constraints. In particular, for sclera image, they propose a new quality measure approach based on a focus measure. In addition, they propose a new fusion method for sclera segmentation which uses pixel properties of both the sclera area and the skin around the eye. Furthermore, sclera template rotation alignment and distance scaling methods are proposed to minimise the error rates when noisy eye images are captured at-a-distance and on-the-move, together with overcoming head pose rotation. Then, a performance analysis on exploited eye images using the Excellent, the Good, the Bad, and the Ugly (EGBU) classification technique for image quality is used to evaluate system performance. Eye images captured under relaxed imaging constraints using four camera devices within the UBIRIS.v2 and MICHE mobile databases are utilised to evaluate the proposed sclera recognition system, with the UBIRIS.v1 database as a reference. Results in terms of sclera image quality measure and sclera segmentation are promising and describe the effect and challenges of using relaxed imaging conditions on sclera recognition system.