2016_TNNLS_Learning Kernel Extended Dictionary for Face Recognition.pdf

This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Learning Kernel Extended Dictionary for Face Recognition Ke-Kun Huang, Member, IEEE, Dao-Qing Dai, Member, IEEE, Chuan-Xian Ren, Member, IEEE, and Zhao-Rong Lai, Student Member, IEEE Abstract— A sparse representation classifier (SRC) and a kernel discriminant analysis (KDA) are two successful methods for face recognition. An SRC is good at dealing with occlusion, while a KDA does well in suppressing intraclass variations. In this paper, we propose kernel extended dictionary (KED) for face recognition, which provides an efficient way for combining KDA and SRC. We first learn several kernel principal components of occlusion variations as an occlusion model, which can represent the possible occlusion variations efficiently. Then, the occlusion model is projected by KDA to get the KED, which can be computed via the same kernel trick as new testing samples. Finally, we use structured SRC for classification, which is fast as only a small number of atoms are appended to the basic dictionary, and the feature dimension is low. We also extend KED to multikernel space to fuse different types of features at kernel level. Experiments are done on several large-scale data sets, demonstrating that not only does KED get impressive results for nonoccluded samples, but it also handles the occlusion well without overfitting, even with a single gallery sample per subject. Index Terms— Face occlusion, face recognition, kernel discrim- inant analysis (KDA), sparse representation classifier (SRC). I. INTRODUCTION FACE recognition has attracted much attention in imageprocessing, pattern recognition, and computer vision because of its wide range of applications, such as access control and video surveillance [1]. After many years of investigation, face recognition is still very challenging d