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

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.