Face recognition using mixtures of principal components

We introduce an efficient statistical modeling technique called mixture of principal components (MPC). This model is a linear extension to the traditional principal component analysis (PCA) and uses a mixture of eigenspaces to capture data variations. We use the model to capture face appearance variations due to pose and lighting changes. We show that this more efficient modeling leads to improved face recognition performance.