Supervised Sparse Patch Coding towards Misalignment-Robust Face Recognition

We address the challenging problem of face recognition under the scenarios where both training and test data are possibly contaminated with spatial misalignments. A supervised sparse coding framework is developed in this paper towards a practical solution to misalignment-robust face recognition. Each given probe face image is then uniformly divided into a set of local patches. We propose to sparsely reconstruct each probe image patch from the patches of all gallery images, and at the same time the reconstructions for all patches of the probe image are regularized by one term towards enforcing sparsity on the subjects of those selected patches. The derived reconstruction coefficients by ℓ₁-norm minimization are then utilized to fuse the subject information of the patches for identifying the probe face. Such a supervised sparse coding framework provides a unique solution to face recognition. Extensive face recognition experiments on three benchmark face datasets demonstrate the advantages of the proposed framework over holistic sparse coding and conventional subspace learning based algorithms in terms of robustness to spatial misalignments and image occlusions.