Article
Class-Conditional Probabilistic Principal Component Analysis: Application to Gender Recognition
Fecha
2011-06-06Registro en:
Revista Computación y Sistemas; Vol. 14 No. 4
1405-5546
Autor
Bekios Calfa, Juan
Buenaposada, José M.
Baumela, Luis
Institución
Resumen
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 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.