TY - GEN
T1 - Learning the relationship between high and low resolution images in kernel space for face super resolution
AU - Zou, Wilman W.W.
AU - Yuen, Pong C.
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - This paper proposes a new nonlinear face super resolution algorithm to address an important issue in face recognition from surveillance video namely, recognition of low resolution face image with nonlinear variations. The proposed method learns the nonlinear relationship between low resolution face image and high resolution face image in (nonlinear) kernel feature space. Moreover, the discriminative term can be easily included in the proposed framework. Experimental results on CMU-PIE and FRGC v2.0 databases show that proposed method outperforms existing methods as well as the recognition based on high resolution images.
AB - This paper proposes a new nonlinear face super resolution algorithm to address an important issue in face recognition from surveillance video namely, recognition of low resolution face image with nonlinear variations. The proposed method learns the nonlinear relationship between low resolution face image and high resolution face image in (nonlinear) kernel feature space. Moreover, the discriminative term can be easily included in the proposed framework. Experimental results on CMU-PIE and FRGC v2.0 databases show that proposed method outperforms existing methods as well as the recognition based on high resolution images.
UR - http://www.scopus.com/inward/record.url?scp=78149484721&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2010.288
DO - 10.1109/ICPR.2010.288
M3 - Conference proceeding
AN - SCOPUS:78149484721
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 1152
EP - 1155
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
T2 - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Y2 - 23 August 2010 through 26 August 2010
ER -