Locality constrained joint dynamic sparse representation for local matching based face recognition

• Published in: PloS one Vol. 9; no. 11; p. e113198
• Main Authors: Wang, Jianzhong, Yi, Yugen, Zhou, Wei, Shi, Yanjiao, Qi, Miao, Zhang, Ming, Zhang, Baoxue, Kong, Jun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.11.2014; Public Library of Science (PLoS)
• Subjects: Algorithms; Biometric Identification - methods; Biometry; Classification; Collaboration; Computer and Information Sciences; Computer science; Dictionaries; Face; Face - anatomy & histology; Face recognition; Facial recognition technology; Humans; Image classification; Image Interpretation, Computer-Assisted - methods; Information processing; Information technology; Laboratories; Machine learning; Matching; Mathematics; Methods; Object recognition; Pattern recognition; Pattern Recognition, Automated - methods; Principal components analysis; Representations; Reproducibility of Results; Researchers; Signal processing
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0113198

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.