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

Bekios, Juan and Buenaposada Biencinto, José Miguel and Baumela Molina, Luis (2011). Class-Conditional Probabilistic Principal Component Analysis: application to gender recognition. "Computación y Sistemas", v. 14 (n. 4); pp. 383-391. ISSN 1405-5546.

Description

Title: Class-Conditional Probabilistic Principal Component Analysis: application to gender recognition
Author/s:
  • Bekios, Juan
  • Buenaposada Biencinto, José Miguel
  • Baumela Molina, Luis
Item Type: Article
Título de Revista/Publicación: Computación y Sistemas
Date: June 2011
ISSN: 1405-5546
Volume: 14
Subjects:
Freetext Keywords: Clasificación de género; Análisis de caras; PPCA condicionado a la clase; Gender classification; Face analysis; Class conditional PPCA
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTIN2008- 06815-C02-02UnspecifiedUnspecifiedUnspecified
Government of SpainCSD2007-00018UnspecifiedUnspecifiedUnspecified

More information

Item ID: 38546
DC Identifier: http://oa.upm.es/38546/
OAI Identifier: oai:oa.upm.es:38546
Official URL: http://cys.cic.ipn.mx/ojs/index.php/CyS/article/view/1283/1375
Deposited by: Memoria Investigacion
Deposited on: 29 Jan 2016 10:24
Last Modified: 27 Nov 2018 09:05
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