Class-Conditional Probabilistic Principal Component Analysis: application to gender recognition

Este trabajo presenta una solución al problema del reconocimiento del género de un rostro humano a partir de una imagen. Adoptamos una aproximación que utiliza la cara completa a través de la textura de la cara normalizada y redimensionada como entrada a un clasificador Näive Bayes. Presentamos la técnica de Análisis de Componentes Principales Probabilístico Condicionado-a-la-Clase (CC-PPCA) para reducir la dimensionalidad de los vectores de características para la clasificación y asegurar la asunción de independencia para el clasificador. Esta nueva aproximación tiene la deseable propiedad de presentar un modelo paramétrico sencillo para las marginales. Además, este modelo puede estimarse con muy pocos datos. En los experimentos que hemos desarrollados mostramos que CC-PPCA obtiene un 90% de acierto en la clasificación, resultado muy similar al mejor presentado en la literatura---ABSTRACT---This paper presents a solution to the problem of recognizing the gender of a human face from an image. We adopt a holistic approach by using the cropped and normalized texture of the face as input to a Naïve Bayes classifier. First it is introduced the Class-Conditional Probabilistic Principal Component Analysis (CC-PPCA) technique to reduce the dimensionality of the classification attribute vector and enforce the independence assumption of the classifier. This new approach has the desirable property of a simple parametric model for the marginals. Moreover this model can be estimated with very few data. In the experiments conducted we show that using CCPPCA we get 90% classification accuracy, which is similar result to the best in the literature. The proposed method is very simple to train and implement

Improving Naive Bayes using Class-Conditional ICA

In the past years, Naive Bayes has experienced a renaissance in machine learning, particularly in the area of information retrieval. This classifier is based on the not always realistic assumption that class-conditional distributions can be factorized in the product of their marginal densities. On the other side, one of the most common ways of estimating the Independent Component Analysis (ICA) representation for a given random vector consists in minimizing the Kullback-Leibler distance between the joint density and the product of the marginal densities (mutual information). l'om this that ICA provides a representation where the independence assumption can be held on stronger grounds. In this paper we propose class-conditional ICA as a method that provides an adequate representation where Naive Bayes is the classifier of choice. Experiments on two public databases are performed in order to confirm this hypothesis