A Gabor Feature Classifier for Face Recognition
نویسندگان
چکیده
This paper describes a novel Gabor Feature Class$er (GFC) method for face recognition. The GFC method employs an enhanced Fisher discrimination model on an augmented Gabor feature vector; which is derived from the Gabor wavelet transformation o f f i c e images. The Gabor wavelets, whose kernels are similar to the 2 0 receptive field profiles of the nianinialian cortical simple cells, exhibit desirable characteristics of spatial locality and orientation selectivity. As a result, the Gabor transformed face images produce salient local and discriminating features that are suitable forface recognition. The feasibility of the new GFC method has been successfully tested on face recognition using 600 FERET frontal face images, which involve different illumination and varied facial expressions of 200 subjects. The effectiveness of the novel GFC method is shown in ternis of both absolute performance indices and comparative performance against some popular face recognition schemes such as the Eigenfaces method and some other Gabor wavelet based class$cation methods. In particular; the novel GFC method achieves 100% recognition accuracy using only 62features. 1
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