Minimizing Illumination Differences for 3D to 2D Face Recognition Using Lighting Maps

Abstract

Asymmetric 3D to 2D face recognition has gained attention from the research community since the real-world application of 3D to 3D recognition is limited by the unavailability of inexpensive 3D data acquisition equipment. A 3D to 2D face recognition system explicitly relies on 3D facial data to account for uncontrolled image conditions related to head pose or illumination. We build upon such a system, which matches relit gallery textures with pose-normalized probe images, using the gallery facial meshes. The relighting process, however, is based on an assumption of indoor lighting conditions and limits recognition performance on outdoor images. In this paper, we propose a novel method for minimizing illumination difference by unlighting a 3D face texture via albedo estimation using lighting maps. The algorithm is evaluated on challenging databases (UHDB30, UHDB11, FRGC v2.0) with drastic lighting and pose variations. The experimental results demonstrate the robustness of our method for estimating the albedo from both indoor and outdoor captured images, and the effectiveness and efficiency for illumination normalization in face recognition.