Toward Robust Face Recognition from Multiple Views

This paper presents a novel approach to aid face recognition: Using multiple views of a face, we construct a 3D model instead of directly using the 2D images for recognition. Our framework is designed for videos, which contain many instances of a target face from a sequence of slightly differing views, as opposed to a single static picture of the face. Specifically, we reconstruct the 3D face shapes from two orthogonal views and select features based on pairwise distances between landmark points on the model using Fisher's Linear Discriminant. While 3D face shape reconstruction is sensitive to the quality of the feature point localization, our experiments show that 3D reconstruction together with the regularized Fisher's Linear Discriminant can provide highly accurate face recognition from multiple facial views. Experiments on the Carnegie Mellon PIE (Pose, Illumination and Expressions) database containing 68 people’s faces with at least 3 expressions under varying lighting conditions demonstrate vastly improved performance