Directional Illumination Estimation Sets and Multilevel Matching Metric for Illumination-Robust Face Recognition

It is a challenging task to improve the performance of face recognition under complex illumination conditions. Illumination estimation-based illumination invariant extraction is widely used to alleviate the adverse effects of illumination variation on face recognition. Most existing methods only used slowly changing characteristics of lighting to achieve illumination estimation, thus resulting in inaccurate illumination estimation and illumination invariant extraction under complex illumination conditions. To alleviate this issue, on the basis of the Lambertian reflectance model, we propose an innovative method of directional illumination estimation to extract directional illumination invariant sets from a facial image. The directional illumination invariant sets not only better preserve essential features of the face, but also largely reduce adverse effects of rapid light changes. Moreover, we propose a multilevel matching metric for category classification by using an inner product measure and residual matching. Experimental results on Yale B⁺, CAS-PEAL-R1, uncontrolled and AR face databases validate that the proposed method can effectively improve the accuracy of face recognition under complex illumination conditions.