Hybrid face recognition systems for profile views using the MUGSHOT database

Face recognition has established itself as an important sub-branch of pattern recognition within the field of computer science. Many state-of-the-art systems have focused on the task of recognizing frontal views or images with just slight variations in head pose and facial expression of people. We concentrate on two approaches to recognize profile views (90 degrees) with previous knowledge of only the frontal view, which is a challenging task even for human beings. The first presented system makes use of synthesized profile views and the second one uses a joint parameter estimation technique. The systems we present combine artificial neural networks (NN) and a modeling technique based on hidden Markov models (HMM). One of the main ideas of these systems is to perform the recognition task without the use of any 3D-information of heads and faces such as a physical 3D-models, for instance. Instead, we represent the rotation process by a NN, which has been trained with prior knowledge derived from image pairs showing the same person's frontal and profile view. Another important restriction to this task is that we use exactly one example frontal view to train the system to recognize the corresponding profile view for a previously unseen individual. The presented systems are tested with a sub-set of the MUGSHOT database.