Face Recognition using a Hybrid Supervised/Unsupervised Neural Network

A system for automatic face recognition is presented. It consists of several steps; Automatic detection of the eyes and mouth is followed by a spatial normalization of the images. The classification of the normalized images is carried out by a hybrid (supervised and unsupervised) Neural Network. Two methods for reducing the overfitting -- a common problem in high dimensional classification schemes -- are presented, and the superiority of their combination is demonstrated. Key words: Face recognition, Neural Networks, Interest points, Symmetry operator. 1 Introduction Automatic face recognition has gained much attention in recent years, due to the variety of potential applications, and the increase in computational power which enables effective implementation of algorithms. Traditionally, face recognition was based on extracting certain features (e.g. spatial location of facial features and their geometrical relations) [4, 20]. These features are detected either manually, or by autom..