Gaussian Mixture Models Based on Principal Components and Applications

Application of Principal Components Analysis and Gaussian Mixture Models to Printer Identification

Printer identification based on a printed document has many desirable forensic applications. In the electrophotographic process (EP) quasiperiodic banding artifacts can be used as an effective intrinsic signature. However, in text only document analysis, the absence of large midtone areas makes it difficult to capture suitable signals for banding detection. Frequency domain analysis based on th...

Multiresolution Gaussian Mixture Models: Theory and Applications

Pseudo-Gaussian Inference in Heterokurtic Elliptical Common Principal Components Models

The so-called Common Principal Components (CPC) Model, in which the covariance matrices Σi of m populations are assumed to have identical eigenvectors, was introduced by Flury (1984), who develops Gaussian parametric inference methods for this model (Gaussian maximum likelihood estimation and Gaussian likelihood ratio testing). A key result in that context is the joint asymptotic normality of t...

Estimating the Number of Components in Gaussian Mixture Models Adaptively ⋆

An important but difficult problem of mixture model is estimating the number of components, k, by model selection criterion. We investigate the sum of weighted real and imaginary parts of all LogCharacteristic Functions (LCF) for Gaussian Mixture Model (GMM) and propose a new method to estimate k, adaptively. Our method defines the Sum of Weighted Real parts of all LCFs (SWRLCF) as a new conver...

Facial feature points detecting based on Gaussian Mixture Models

Detectingpredefined facial featurepoints in ahuman face image is awell studiedproblem.Despite the impressive achievements that have been made, it is still open under unconstrained environments, with variations of illumination, expression, head pose, as well as partial occlusions. This paper proposed a novel method to locate facial feature points under the variations mentioned above. Support vec...