Combined Classifiers for Invariant Face Recognition

Abstract

We present a system for invariant face recognition. A combined classifier uses the generalization capabilities of both learning vector quantization (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 generalization 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 recognizing a face in less than one second. The system is tested on the well-known ORL database. The system performance compares favorably 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. This rate compares favorably with the rates achieved by other systems on the same database. The volumetric frequency domain representation resulted in a rate of 92.5% while the combination of a convolutional neural network and self-organizing map resulted in 96.2% for the same number of training faces (five) per person in a database representing 40 people.