%0 Journal Article
%T Class-Conditional Probabilistic Principal Component Analysis: Application to Gender Recognition
%A Bekios Calfa
%A Juan
%A Buenaposada
%A Jos¨¦ M.
%A Baumela
%A Luis
%J Computaci¨®n y Sistemas
%D 2011
%I Instituto Polit¨¦cnico Nacional
%X 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 na¨ªve 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.
%K gender classification
%K face analysis
%K class conditional ppca.
%U http://www.scielo.org.mx/scielo.php?script=sci_abstract&pid=S1405-55462011000200005&lng=en&nrm=iso&tlng=en