LEARNING ASSOCIATE APPEARANCE MANIFOLDS FOR CROSS-POSE FACE RECOGNITION

Pose variation is a major challenge in face recognition. In this paper, we propose a novel cross-pose face recognition method by learning associate appearance manifolds to model the connection of faces under different poses. The associate manifolds are built on an auxiliary set, in which each identity contains cross-pose face images. The basic assumption is that cross-pose face images from two similar identities can be projected onto similar appearance manifolds by pose-specific transforms. We first associate the input faces with alike identities from the auxiliary set. Then the manifolds of cross-pose faces in the training set are confined close to that of the associate identities in the auxiliary set. Thus, the connection of cross-pose faces is well modeled by the associate appearance manifolds on the auxiliary set. Formally, we formulate the assumption as a manifold-based distance minimization problem, so as to learn the optimal transforms. Experiments on the Multi-PIE dataset demonstrate the effectiveness of the proposed method.