Modeling Gabor Coefficients via Generalized Gaussian Distributions for Face Recognition

Gabor filters are biologically motivated convolution kernels that have been widely used in the field of computer vision and, specially, in face recognition during the last decade. This paper proposes a statistical model of Gabor coefficients extracted from face images using generalized Gaussian dist...

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Bibliographic Details
Main Authors: Gonzalez-Jimenez, D., Perez-Gonzalez, F., Comesana-Alfaro, P., Perez-Freire, L., Alba-Castro, J.L.
Format: Conference Proceeding
Language:English
Subjects:
Biological system modeling
Computer vision
Convolution
data compression
Data mining
Face detection
Face recognition
Frequency measurement
Gabor filters
Gaussian distribution
Generalized Gaussian Distribution
Kernel
Kullback-Leibler distance
Lloyd-Max quantization
XM2VTS database
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Summary:Gabor filters are biologically motivated convolution kernels that have been widely used in the field of computer vision and, specially, in face recognition during the last decade. This paper proposes a statistical model of Gabor coefficients extracted from face images using generalized Gaussian distributions (GGD's). By measuring the Kullback-Leibler distance (KLD) between the pdf of the GGD and the relative frequency of the coefficients, we conclude that GGD's provide an accurate modeling. The underlying statistics allow us to reduce the required amount of data to be stored (i.e. data compression) via Lloyd-Max quantization. Verification experiments on the XM2VTS database show that performance does not drop when, instead of the original data, we use quantized coefficients.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2007.4380060