A Lighting Normalization Approach Exploiting Face Symmetry

Lighting normalization is a kind of widely used approach for achieving illumination invariant face recognition. Lighting normalization approaches try to regularize various lighting conditions in different face images into ideal illumination before face recognition. However, many existing methods perform lighting normalization by treating face images as natural images, and neglect the particular properties of faces, e.g. face symmetry. As a result, for the face images with side lighting, many existing methods cannot recover the facial features in shadow regions. To resolve this problem, in this paper, a novel lighting normalization approach exploiting face symmetry priori is proposed for illumination invariant face recognition. In the proposed approach, lighting normalization for a shadow region is performed by referring to the face structure information of a symmetric non-shadow region. The symmetry priori of face structure is modeled via an energy minimization framework. In addition, a shadow-free reliability map is further proposed to simplify the original bivariate optimization problem into a univariate one in order to reduce the computation cost. Experiments on face images with synthetic and real shadows show that the proposed lighting normalization approach is effective in recovering facial features in shadow regions of a face, and also robust to face misalignment and asymmetric face geometric normalization.