Face Virtual Pose Generation using Aligned Locally Linear Regression for Face Recognition
In this paper a new solution for the single sample problem in low resolution face recognition is proposed. The proposed solution uses an enhanced virtual pose generation method to extend the number of face images of each identity. Using a top-right face image of an identity in the gallery, the method generates other poses of the same identity. Face images are represented as a set of local patches. In order to avoid image alignment problems, patches in the first and second pose are clustered. For each cluster the mapping between the patches of the two poses is learned. Experimental results show superior subjective and objective performance of the proposed method on CASPEAL database compared to earlier local and global methods.
