Spectacle problem removal from facial images based on detail preserving filtering schemes

Abstract

 Accurate detection and localization of eye features under spectacles - is quite a relevant but challenging problem in the application field of Human-Computer Interactive (HCI) systems. The ill-effects caused by the usage of spectacles like occlusion, glare and secondary reflection formation are termed as “The Spectacle Problem.” In this paper, the authors alleviate the spectacle problem by employing a two-image based data fusion approach. Detail-preserving filters like Joint Bilateral Filter (JBF) and Guided Image Filter (GIF) are compared individually, to find out the suitability and consistency of the filters in the proposed data fusion approach. Experimentation on CASIA NIR-VIS 2.0 and self-generated facial database demonstrate that GIF based filtering approach has higher local and global eye feature preservation capability while mitigating the spectacle problem.