Mining discriminative components with low-rank and sparsity constraints for face recognition

This paper introduces a novel image decomposition approach for an ensemble of correlated images, using low-rank and sparsity constraints. Each image is decomposed as a combination of three components: one common component, one condition component, which is assumed to be a low-rank matrix, and a sparse residual. For a set of face images of N subjects, the decomposition finds N common components, one for each subject, K low-rank components, each capturing a different global condition of the set (e.g., different illumination conditions), and a sparse residual for each input image. Through this decomposition, the proposed approach recovers a clean face image (the common component) for each subject and discovers the conditions (the condition components and the sparse residuals) of the images in the set. The set of N + K images containing only the common and the low-rank components form a compact and discriminative representation for the original images. We design a classifier using onl...

• Type: Journal
• Year: 2012
• Where: KDD
• Authors: Qiang Zhang, Baoxin Li
• Keywords: Data Mining, Decomposition Approach, Illumination Conditions, Information Systems Applications, KDD 2012