Recent advances in face biometrics with Gabor wavelets: A review
Pattern Recognition Letters
Multi-expression face recognition using neural networks and feature approximation
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Gabor feature based classification using 2d linear discriminant analysis for face recognition
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Face recognition by discriminant analysis with gabor tensor representation
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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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.