An Adaptive Combined Classifier System for Invariant Face Recognition

In classification tasks it may be wise to combine observations from different sources. In this paper, to obtain classification systems with both good generalization performance and efficiency in space and time, a learning vector quantization learning method based on combinations of weak classifiers is proposed. The weak classifiers are generated using automatic elimination of redundant hidden layer neurons of the network on both the entire face images and the extracted features: forehead, right eye, left eye, nose, mouth, and chin. The neuron elimination is based on the killing of blind neurons, which are redundant. The classifiers are then combined through majority voting on the decisions available from input classifiers. It is demonstrated that the proposed system is capable of achieving better classification results with both good generalization performance and a fast training time on a variety of test problems using a large and variable database. The selection of stable and representative sets of features that efficiently discriminate between faces in a huge database is discussed.  2002 Elsevier Science (USA)