| Paper: | TP-P7.5 |
| Session: | Biometrics IV: Face Recognition |
| Time: | Tuesday, September 18, 14:30 - 17:10 |
| Presentation: |
Poster
|
| Title: |
MODELING GABOR COEFFICIENTS VIA GENERALIZED GAUSSIAN DISTRIBUTIONS FOR FACE RECOGNITION |
| Authors: |
Daniel Gonzalez-Jimenez; University of Vigo | | |
| | Fernando Perez-Gonzalez; University of Vigo | | |
| | Pedro Comesana-Alfaro; University of Vigo | | |
| | Luis Perez-Freire; University of Vigo | | |
| | Jose Luis Alba-Castro; University of Vigo | | |
| Abstract: |
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 \textit{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. |
