Combined Classifiers for Invariant Face Recognition

Abstract.

This paper presents a system for invariant face recognition. A combined classifier uses the generalisation capabilities of both Learning Vector Quantisation (LVQ) and Radial Basis Function (RBF) neural networks to build a representative model of a face from a variety of training patterns with different poses, details and facial expressions. The combined generalisation error of the classifier is found to be lower than that of each individual classifier. A new face synthesis method is implemented for reducing the false acceptance rate and enhancing the rejection capability of the classifier. The system is capable of recognising a face in less than one second. The well-known ORL database is used for testing the combined classifier. Comparisons with several other systems show that our system compares favourably with the state-of-the-art systems. In the case of the ORL database, a correct recognition rate of 99.5% at 0.5% rejection rate is achieved.