FUZZY INFORMATION FUSION IN A FACE RECOGNITION SYSTEM

Abstract

We describe and evaluate information fusion by fuzzy integration in a robust, high performance face recognition system. The system uses fuzzy integrals to combine classifiers operating at different image resolutions. Recognition is carried out by distance classification of transformed vectors of local autocorrelation coefficients. The transformation is determined by linear discriminant analysis. A large database of 11,600 images of 116 persons is used to determine the system performance. After being trained to recognize 60 persons, it is tested on images of all persons in the database. Both training and test stages use 50 images of each person. Under two different training schemes, it achieves peak recognition rates of 98.4% and 97.9%, respectively, accepting only 1.6% and 2.4% of the unknown faces. This exceeds the performance of any of the individual classifiers by at least 10%. Moreover, it exceeds earlier results obtained by multiple resolution averaging on the same database by at least 1.0%.