Adaptive LVQ Classifier for Invariant Face Recognition

Abstract

Visual communication plays an important role in human communication and interaction. In order to interact socially, we must be able to process faces in a variety of ways. This paper addresses the issue of recognition using a compact architecture of a learning vector quantization classifier that learns the correlation of patterns and identifies human faces. The suggested neural network structure is shown to exhibit robustness in achieving better classification results with both good generalization performance and a fast training time on a variety of test problems using standard and variable databases. Sources of variability include facial expression, gender, individual appearance, tilt, lighting conditions, and occluding objects (hair, spectacles, etc). Empirical results yield a peak accuracy rate of 100% (99.63% average) on the face recognition task for a random test set of 90 face images of 30 subjects from the Kuwait University databases on the network that is trained with another set of 60 faces of the same subjects. The computer execution time is about 0.04 s per face image on a Pentium II, 233 MHz, 32 MB, and Win98 PC.