TY - GEN
T1 - Face recognition using landmark-based bidimensional regression
AU - Jiazheng, Shi
AU - Samal, Ashok
AU - Marx, David
PY - 2005
Y1 - 2005
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=34548566212&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34548566212&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2005.61
DO - 10.1109/ICDM.2005.61
M3 - Conference contribution
AN - SCOPUS:34548566212
SN - 0769522785
SN - 9780769522784
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 765
EP - 768
BT - Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
T2 - 5th IEEE International Conference on Data Mining, ICDM 2005
Y2 - 27 November 2005 through 30 November 2005
ER -