Abstract
Iris center localization is the basis of iris biometrics, face recognition and gaze tracking. However, individual differences, changes in facial expression, varying light conditions, occlusion, and so on, all bring great challenges to accurately localize the iris center. In order to improve localization accuracy in low-quality images and meet the need of efficiency in practical applications, a novel method of iris center localization is proposed in this paper using energy map synthesis based on image gradient, image inpaint technology, and post-processing correction. The image inpaint technology is firstly adopted to inhibit the effect of some specular reflection. Then the energy maps based on image gradient and eye ROI (Region Of Interest) midpoint are synthesized to significantly improve the localization accuracy. In the end, post-processing correction is carried out to eliminate influence of the closed eye and other large derivations to further improve the localization accuracy. The algorithm is verified on the challenging BioID database, Talking Face Video database and the MUCT face database. The result shows the localization accuracy has outperformed the state-of-the-art unsupervised methods on the three databases, and it is suitable for real-time applications.