Resumo

BEKIOS CALFA, Juan; BUENAPOSADA, José M.  e  BAUMELA, Luis. Class-Conditional Probabilistic Principal Component Analysis: Application to Gender Recognition. Comp. y Sist. [online]. 2011, vol.14, n.4, pp.383-391. ISSN 2007-9737.

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.

Palavras-chave : Gender classification; face analysis; class conditional PPCA.

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