Class–Conditional Probabilistic Principal Component Analysis: Application to Gender Recognition

Análisis de componentes principales probabilístico condicionado a la clase: aplicación al reconocimiento de género

Juan Bekios Calfa¹, José M. Buenaposada² and Luis Baumela³

¹ Dept. de Ing. de Sistemas y Computación, Universidad Católica del Norte, Av. Angamos 0610, Antofagasta, Chile. juan.bekios@ucn.cl.

³ Dept. de Inteligencia Artificial, Universidad Politécnica de Madrid, Campus Montegancedo s/n, 28660 Boadilla del Monte, Spain. lbaumela@fi.upm.es, http://www.dia.fi.upm.es/~pcr

Article received on January 29, 2010
Accepted on June 17, 2010

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 Naíve 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 CC–PPCA 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.

Keywords: Gender classification, face analysis, class conditional PPCA.

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 Náive 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. El modelo propuesto es muy sencillo de entrenar e implementar.

Palabras clave: Clasificación de género, análisis de caras, PPCA condicionado a la clase.

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Acknowledgments

José M. Buenaposada and Luis Baumela gratefully acknowledge funding from the Spanish Ministerio de Educación y Ciencia under contract TIN2008–06815–C02–02. Juan Bekios–Calfa and Luis Baumela were also funded by Spanish research programme Consolider–Ingenio 2010: MIPRCV CSD2007–00018.

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