Face recognition using a new distance metric

Many classification techniques use a distance metric as a measure of the similarity between patterns, and their generalisation performance is often strongly related to the effectiveness of the measure. This paper introduces a distance metric based on the Mahalanobis distance function, which is statistically more reliable than some metrics but does not discard discriminating information, often regarded as "noise". In addition, it may be computed quickly. This paper develops this metric and experimentally shows that it may be used in a classifier to give the lowest error rate (2.63%) as well as the best training and classification times for a face recognition task.