Cluster-Classification Bayesian Networks for head pose estimation

Head pose estimation is critical in many applications such as face recognition and human-computer interaction. Various classifiers such as LDA, SVM, or nearest neighbor are widely used for this purpose; however, the recognition rates are limited due to the limited discriminative power of these classifiers for discretized pose estimation. In this paper, we propose a head pose estimation method using a Cluster-Classification Bayesian Network (CCBN), specifically designed for classification after clustering. A pose layout is defined where similar poses are assigned to the same block. This increases the discriminative power within the same block when similar yet different poses are present. We achieve the highest recognition accuracy on two public databases (CAS-PEAL and FEI) compared to the state-of-the-art methods.