Metaface learning for sparse representation based face recognition

Meng Yang, Lei Zhang, Jian Yang, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

317 Citations (Scopus)


Face recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This paper discusses the metaface learning (MFL) of face images under the framework of SRC. Although directly using the training samples as dictionary bases can achieve good FR performance, a well learned dictionary matrix can lead to higher FR rate with less dictionary atoms. An SRC oriented unsupervised MFL algorithm is proposed in this paper and the experimental results on benchmark face databases demonstrated the improvements brought by the proposed MFL algorithm over original SRC.
Original languageEnglish
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Number of pages4
Publication statusPublished - 1 Dec 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: 26 Sep 201029 Sep 2010


Conference2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/TerritoryHong Kong
CityHong Kong


  • Face recognition
  • Metaface learning
  • Sparse representation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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