TY - GEN
T1 - Gabor wavelets and kernel direct discriminant analysis for face recognition
AU - Shen, Linlin
AU - Bai, Li
PY - 2004
Y1 - 2004
N2 - A novel Gabor-Kernel face recognition method is proposed in this paper. This involves convolving a face image with a series of Gabor wavelets at different scales, locations, and orientations and extracting features from resulting Gabor filtered images. Kernel Discriminant Analysis (KDDA) is then applied to the feature vectors for dimension reduction as well as class separability enhancement. A database of 600 frontal-view face images from the FERET face database is used to test the method. Experimental results demonstrate the advantage of KDDA over other Kernel methods such as Kernel Principal Component Analysis (KPCA) and General Discriminant Analysis (GDA). Significant improvements are also observed when features are extracted from Gabor filtered images instead of the original images. A 94% accuracy has been observed for the novel Gabor + KDDA method on the FERET database using a simple classifier, which could be further improved by employing a more complex classifier and distance measurer.
AB - A novel Gabor-Kernel face recognition method is proposed in this paper. This involves convolving a face image with a series of Gabor wavelets at different scales, locations, and orientations and extracting features from resulting Gabor filtered images. Kernel Discriminant Analysis (KDDA) is then applied to the feature vectors for dimension reduction as well as class separability enhancement. A database of 600 frontal-view face images from the FERET face database is used to test the method. Experimental results demonstrate the advantage of KDDA over other Kernel methods such as Kernel Principal Component Analysis (KPCA) and General Discriminant Analysis (GDA). Significant improvements are also observed when features are extracted from Gabor filtered images instead of the original images. A 94% accuracy has been observed for the novel Gabor + KDDA method on the FERET database using a simple classifier, which could be further improved by employing a more complex classifier and distance measurer.
UR - http://www.scopus.com/inward/record.url?scp=10044250912&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2004.1334108
DO - 10.1109/ICPR.2004.1334108
M3 - Conference contribution
AN - SCOPUS:10044250912
SN - 0769521282
T3 - Proceedings - International Conference on Pattern Recognition
SP - 284
EP - 287
BT - Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
A2 - Kittler, J.
A2 - Petrou, M.
A2 - Nixon, M.
T2 - Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
Y2 - 23 August 2004 through 26 August 2004
ER -