Face Recognition Using Landmark-based Bidimensional Regression Jiazheng Shi, Ashok Samal, and David Marx   View Paper (PDF)   Return to Session 14: Data Mining Applications 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. ©2006 Association for Computing Machinery