Distinctive Personal Traits for Face Recognition Under Occlusion

Existing local feature methods for face recognition utilize visually salient regions around eye, nose, and mouth to model the characteristics of a person. The premise of such an approach is that there exists a set of features that are common in all human faces and yet distinct to tell one from the rest apart. In this paper we present an algorithm that selects the best set of features or templates for each individual, and uses these distinct personal traits to boost face recognition performance even when they are partially occluded. Borne out by numerous experiments and comparisons, we demonstrate that the proposed method is effective in recognizing faces with partial occlusion and variation in expression.