Abstract
Pose-invariant face recognition using single frontal training image is considered one of the most difficult challenges in face recognition. To address this problem, we introduce a. novel feature extraction method based on learning the manifold of local features. Changes in local features clue to pose variations induce a. nonlinear manifold in the feature space. Self-organizing map is employed to learn the manifold induced by Gabor filter response of a generic training face database captured at various pose directions. Furthermore, this manifold can be used to represent new face image its a set of points in the feature space. A modular Hausdorff distance measure, which can effectively measure the similarity between two point sets without any correspondence: is also proposed to identify unlabeled subjects. Experimental results on CMU-PIE face database show the effectiveness of the novel method against pose variations.