title: Locality constrained joint dynamic sparse representation for local matching based face recognition.
abstract: Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC.
authors.affiliation: College of Computer Science and Information Technology, Northeast Normal University, Changchun, China; National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun, China.
authors.forename: Jianzhong
authors.initials: J
authors.lastname: Wang
authors.affiliation: College of Computer Science and Information Technology, Northeast Normal University, Changchun, China; School of Mathematics and Statistics, Northeast Normal University, Changchun, China.
authors.forename: Yugen
authors.initials: Y
authors.lastname: Yi
authors.affiliation: College of Computer Science and Information Technology, Northeast Normal University, Changchun, China; Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhou
authors.affiliation: School of Mathematics and Statistics, Northeast Normal University, Changchun, China.
authors.forename: Yanjiao
authors.initials: Y
authors.lastname: Shi
authors.affiliation: College of Computer Science and Information Technology, Northeast Normal University, Changchun, China.
authors.forename: Miao
authors.initials: M
authors.lastname: Qi
authors.affiliation: Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Zhang
authors.affiliation: School of Mathematics and Statistics, Northeast Normal University, Changchun, China; School of Statistics, Capital University of Economics and Business, Beijing, China.
authors.forename: Baoxue
authors.initials: B
authors.lastname: Zhang
authors.affiliation: College of Computer Science and Information Technology, Northeast Normal University, Changchun, China; Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Kong
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results