Distance measures for PCA-based face recognition

In this article we compare 14 distance measures and their modifications between feature vectors with respect to the recognition performance of the principal component analysis (PCA)-based face recognition method and propose modified sum square error (SSE)-based distance. Recognition experiments were performed using the database containing photographies of 423 persons. The experiments showed, that the proposed distance measure was among the first three best measures with respect to different characteristics of the biometric systems. The best recognition results were achieved using the following distance measures: simplified Mahalanobis, weighted angle-based distance, proposed modified SSE-based distance, angle-based distance between whitened feature vectors. Using modified SSE-based distance we need to extract less images in order to achieve 100% Cumulative recognition than using any other tested distance measure. We also showed that using the algorithmic combination of distance measures we can achieve better recognition results than using the distances separately. (C) 2004 Elsevier B.V. All rights reserved.