Collaborative reconstruction-based manifold-manifold distance for face recognition with image sets

In this paper, we propose a new collaborative reconstruction-based manifold-manifold distance (CRMMD) method for face recognition with image sets, where each gallery and probe sample is a set of face images captured from varying poses, illuminations and expressions. Given each face image set, we first model it as a nonlinear manifold and then the recognition task is converted as a manifold-manifold matching problem. For each manifold, we divide it into several clusters and describe each cluster by using a local model. Then, we use the local models from each gallery manifold to collaboratively reconstruct each local model of the testing manifold and the minimal reconstruction error is used for classification. Experimental results on three widely used face datasets are presented to show the effectiveness of the proposed method.