Joint sparsity model with matrix completion for an ensemble of face images

Qiang Zhang, Baoxin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

An ensemble of correlated signals are often encountered in many applications of image processing, such as a set of face images of the same subject. In this paper, we propose a new model, called Joint Sparsity Model with Matrix Completion (JSM-MC), which extracts a common component, an innovation component, and a low-rank component from an ensemble of face images. These components have their respective physical significance in terms of representing different types of information in the original ensemble, hence facilitating an analysis task such as recognition. An algorithm is proposed under the model to solve for the components, based on Block Coordinate Descent and Singular Value Thresholding. Experimental results show that the proposed method has unique advantages over existing methods in dealing with challenging face images with extreme illumination conditions or occlusions.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages1665-1668
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: Sep 26 2010Sep 29 2010

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2010 17th IEEE International Conference on Image Processing, ICIP 2010
CountryHong Kong
CityHong Kong
Period9/26/109/29/10

Keywords

  • Compressive sensing
  • Face image
  • Joint sparsity
  • Matrix completion

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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