Anti-Glare: Tightly Constrained Optimization for Eyeglass Reflection Removal

Tushar Sandhan, Jin Young Choi; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1241-1250

Abstract


Absence of a clear eye visibility not only degrades the aesthetic value of an entire face image but also creates difficulties in many computer vision tasks. Even mild reflections produce the undesired superpositions of visual information, whose decomposition into the background and reflection layers using a single image is a highly ill-posed problem. In this work, we enforce the tight constraints derived by thoroughly analysing the properties of an eyeglass reflection. In addition, our strategy regularizes gradients of the reflection layer to be highly sparse and proposes the facial symmetry prior via formulating a non-convex optimization scheme, which removes the reflections within a few iterations. Experiments on frontal face image inputs demonstrate the high quality reflection removal results and improvement of the iris detection rate.

Related Material


[pdf] [poster]
[bibtex]
@InProceedings{Sandhan_2017_CVPR,
author = {Sandhan, Tushar and Young Choi, Jin},
title = {Anti-Glare: Tightly Constrained Optimization for Eyeglass Reflection Removal},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}