Using sparse regression to learn effective projections for face recognition

Yongxin Taylor Xi, Peter Jeffrey Ramadge

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

4 Scopus citations

Abstract

We explore sparse regression for effective feature selection and classification in face identity and expression recognition. We argue that sparse regression in pixel space is inappropriate. We propose instead a method which combines the virtues of sparse regression with projection methods such as PCA and FDA. The method can learn a sparse set of discriminative projections and increase recognition accuracy beyond that achievable by FDA.We demonstrate this by performance comparisons on three face data sets.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
PublisherIEEE Computer Society
Pages3333-3336
Number of pages4
ISBN (Print)9781424456543
DOIs
StatePublished - Jan 1 2009
Event2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt
Duration: Nov 7 2009Nov 10 2009

Publication series

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

Other

Other2009 IEEE International Conference on Image Processing, ICIP 2009
CountryEgypt
CityCairo
Period11/7/0911/10/09

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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