2012 MiningDiscriminativeComponentsw

• (Zhang & Li, 2012) ⇒ Qiang Zhang, and Baoxin Li. (2012). “Mining Discriminative Components with Low-rank and Sparsity Constraints for Face Recognition.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339760

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Mining+Discriminative+Components+with+Low-rank+and+Sparsity+Constraints+for+Face+Recognition
• http://dl.acm.org/citation.cfm?id=2339530.2339760&preflayout=flat#citedby

Quotes

Author Keywords

• Component decomposition; face recognition; low-rank matrix; miscellaneous; sparse matrix; subspace learning

Abstract

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 only these N + K images. Experiments on commonly-used face data sets demonstrate the effectiveness of the approach for face recognition through comparing with the leading state-of-the-art in the literature. The experiments further show good accuracy in classifying the condition of an input image, suggesting that the components from the proposed decomposition indeed capture physically meaningful features of the input.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 MiningDiscriminativeComponentsw	Qiang Zhang Baoxin Li			Mining Discriminative Components with Low-rank and Sparsity Constraints for Face Recognition				10.1145/2339530.2339760		2012