Face Recognition based on Gradient Gabor feature
Abstract
In this paper, a novel Gradient Gabor (GGabor) filter is proposed to extract multi-scale and multi-orientation features to represent and classify faces. Gradient Gabor combines the derivative of Gaussian functions and the harmonic functions to capture the features in both spatial and frequency domains to deliver orientation and scale information. The spatial positions are combined into Gaussian derivatives which allows it to provide more stable information. An Efficient Kernel Fisher analysis method is proposed to find multiple subspaces based on both GGabor magnitude and phase features, which is a local kernel mapping ...
View more >In this paper, a novel Gradient Gabor (GGabor) filter is proposed to extract multi-scale and multi-orientation features to represent and classify faces. Gradient Gabor combines the derivative of Gaussian functions and the harmonic functions to capture the features in both spatial and frequency domains to deliver orientation and scale information. The spatial positions are combined into Gaussian derivatives which allows it to provide more stable information. An Efficient Kernel Fisher analysis method is proposed to find multiple subspaces based on both GGabor magnitude and phase features, which is a local kernel mapping method to capture the structure information in faces. Experiments on two face databases, FRGC Version 1 and FRGC Version 2, are conducted to compare the performances of the Gabor and GGabor features, which show that GGabor can also be a powerful tool to model faces, and the Efficient Kernel Fisher classifier can improve the efficiency of the original kernel fisher method.
View less >
View more >In this paper, a novel Gradient Gabor (GGabor) filter is proposed to extract multi-scale and multi-orientation features to represent and classify faces. Gradient Gabor combines the derivative of Gaussian functions and the harmonic functions to capture the features in both spatial and frequency domains to deliver orientation and scale information. The spatial positions are combined into Gaussian derivatives which allows it to provide more stable information. An Efficient Kernel Fisher analysis method is proposed to find multiple subspaces based on both GGabor magnitude and phase features, which is a local kernel mapping method to capture the structure information in faces. Experiments on two face databases, FRGC Version 1 and FRGC Version 2, are conducted to compare the performances of the Gabor and GGabor features, which show that GGabor can also be a powerful tool to model faces, and the Efficient Kernel Fisher classifier can improve the efficiency of the original kernel fisher method.
View less >
Conference Title
Proceedings - International Conference on Image Processing, ICIP
Copyright Statement
© 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.