Face recognition using supervised probabilistic principal component analysis mixture model in dimensionality reduction without loss framework

In this study, first a supervised version for probabilistic principal component analysis mixture model is proposed. Using this model, local linear underlying manifolds of data samples are obtained. These underlying manifolds are used in a dimensionality reduction without loss framework, for face recognition application. In this framework, the benefits of dimensionality reduction are used in the predictive model, while using the projection penalty idea, the loss of useful information will be minimised. The authors use support vector machine (SVM) and k-nearest neighbour (KNN) classifiers as the predictive models in this framework. To train and evaluate the proposed method, the well-known face databases are used. The experimental results show that the proposed method with SVM as the predictive model have the most average classification accuracy compared with many traditional methods which use predictive model SVM after dimensionality reduction, and also compared with the projection penalty idea used for linear and non-linear kernel-based dimensionality reduction methods. Moreover, their experiments show that the proposed method with KNN as predictive model is superior to the case that dimensionality reduction is performed, and then the KNN classifier is applied.