TY - GEN
T1 - Feature selection and facial recognition with sparse multiclass classification
AU - Liu, Zhenqiu
AU - Liu, Amy
PY - 2011
Y1 - 2011
N2 - Facial recognition is a popular field of image analysis that has seen a lot of attention in recent years due to its many biometric and commercial applications. In this project, we propose a sparse multi-label classification approach for face identification and feature selection. Our approach is evaluated with two Yale face databases downloaded from the web site: http://www.face-rec.org/databases/. The performance of the proposed approach will be compared with the eigenface and linear discrimination analysis approaches described in contemporary literature.
AB - Facial recognition is a popular field of image analysis that has seen a lot of attention in recent years due to its many biometric and commercial applications. In this project, we propose a sparse multi-label classification approach for face identification and feature selection. Our approach is evaluated with two Yale face databases downloaded from the web site: http://www.face-rec.org/databases/. The performance of the proposed approach will be compared with the eigenface and linear discrimination analysis approaches described in contemporary literature.
UR - http://www.scopus.com/inward/record.url?scp=84864924111&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864924111&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84864924111
SN - 9781601321916
T3 - Proceedings of the 2011 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2011
SP - 970
EP - 972
BT - Proceedings of the 2011 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2011
T2 - 2011 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2011
Y2 - 18 July 2011 through 21 July 2011
ER -