Face Recognition Using Landmark-based Bidimensional Regression

Authors

Jiazheng Shi, University of MichiganFollow
Ashok Samal, University of Nebraska-LincolnFollow
David Marx, University of Nebraska-LincolnFollow

Date of this Version

2005

Comments

Published in Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM’05) (4 p.). Copyright 2005, IEEE. Used by permission.

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.