Face Recognition Across Poses Using a Single 3D Reference Model

Abstract

Approaches for cross-pose face recognition can be split into 2D image based and 3D model based. Many 2D based methods are reported with promising performance but can only work for poses same as those in the training set. Although 3D based methods can handle arbitrary poses, only a small number of approaches are available. Extended from a latest face reconstruction method using a single 3D reference model, this study focuses on using the reconstructed 3D face for recognition. The reconstructed 3D face allows the generation of multi-pose samples for recognition. The recognition performance varies with poses, the closer the pose to the frontal, the better the performance attained. Several ways to improve the performance are attempted, including different numbers of fiducial points for alignment, multiple reference models considered in the reconstruction phase, and both frontal and profile poses available in the gallery. These attempts make this approach competitive to the state-of-the-art methods.