Face recognition using landmark-based bidimensional regression

Shi Jiazheng; Ashok Samal; David Marx

doi:10.1109/ICDM.2005.61

Face recognition using landmark-based bidimensional regression

Shi Jiazheng, Ashok Samal, David Marx

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 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 language	English (US)
Title of host publication	Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
Pages	765-768
Number of pages	4
DOIs	https://doi.org/10.1109/ICDM.2005.61
State	Published - 2005
Event	5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, United States Duration: Nov 27 2005 → Nov 30 2005

Publication series

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

Conference

Conference	5th IEEE International Conference on Data Mining, ICDM 2005
Country/Territory	United States
City	Houston, TX
Period	11/27/05 → 11/30/05

ASJC Scopus subject areas

Engineering(all)

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10.1109/ICDM.2005.61

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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., 1565777, Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 765-768, 5th IEEE International Conference on Data Mining, ICDM 2005, Houston, TX, United States, 11/27/05. https://doi.org/10.1109/ICDM.2005.61

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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.",

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year = "2005",

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language = "English (US)",

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series = "Proceedings - IEEE International Conference on Data Mining, ICDM",

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AB - 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.

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ER -

Face recognition using landmark-based bidimensional regression

Abstract

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