Enhanced Pictorial Structures for Precise Eye Localization Under Incontrolled Conditions

Abstract

In this paper, we present an enhanced pictorial structure (PS) model for precise eye localization, a fundamental problem involved in many face processing tasks. PS is a computationally efficient framework for part-based object modelling. For face images taken under uncontrolled conditions, however, the traditional PS model is not flexible enough for handling the complicated appearance and structural variations. To extend PS, we 1) propose a discriminative PS model for a more accurate part localization when appearance changes seriously, 2) introduce a series of global constraints to improve the robustness against scale, rotation and translation, and 3) adopt a heuristic prediction method to address the difficulty of eye localization with partial occlusion. Experimental results on the challenging LFW (Labeled Face in the Wild) database show that our model can locate eyes accurately and efficiently under a broad range of uncontrolled variations involving poses, expressions, lightings, camera qualities, occlusions, etc.