A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory
Elements of information theory
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Universal Analytical Forms for Modeling Image Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
A review on Gabor wavelets for face recognition
Pattern Analysis & Applications
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Analytical form for a Bayesian wavelet estimator of images using the Bessel K form densities
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
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Gabor filters have demonstrated their effectiveness in automatic face recognition. However, one drawback of Gabor-based face representations is the huge amount of data that must be stored. One way to reduce space is to quantize Gabor coefficients using an accurate statistical model which should reflect the behavior of the data. Statistical image analysis has revealed one interesting property: the non-Gaussianity of marginal statistics when observed in a transformed domain (like Discrete Cosine Transform, wavelet decomposition, etc.). Two models that have been used to characterize this non-normal behavior are the Generalized Gaussian (GG) and the Bessel K Form densities. This paper provides an empirical comparison of both statistical models in the specific scenario of modeling Gabor coefficients extracted from face images. Moreover, an application for biometric template reduction is presented: based on the underlying statistics, compression is first achieved via Lloyd-Max algorithm. Afterwards, only the best nodes of the grid are preserved using a simple feature selection strategy. Templates are reduced to less than 2 Kbytes with drastical improvements in performance, as demonstrated on the XM2VTS database.