Face recognition using landmark-based bidimensional regression

Shi Jiazheng, Ashok Samal, David Marx

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

9 Scopus citations

Abstract

This paper studies how biologically meaningful landmarks extracted from face images can be exploited for face recognition using the bidimensional regression. Incorporating the correlation statistics of landmarks, this paper also proposes a new approach called eigenvalue weighted bidimensional regression. Complex principal component analysis is used for computing eigenvalues and removing correlation among landmarks. We evaluate our approach using two standard face databases: the Purdue AR and the NIST FERET. Experimental results show that the bidimensional regression is an efficient method to exploit geometry information of face images.

Original languageEnglish (US)
Title of host publicationProceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
Pages765-768
Number of pages4
DOIs
StatePublished - 2005
Event5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, United States
Duration: Nov 27 2005Nov 30 2005

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference5th IEEE International Conference on Data Mining, ICDM 2005
CountryUnited States
CityHouston, TX
Period11/27/0511/30/05

ASJC Scopus subject areas

  • Engineering(all)

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    Jiazheng, S., Samal, A., & Marx, D. (2005). Face recognition using landmark-based bidimensional regression. In Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005 (pp. 765-768). [1565777] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2005.61