Face recognition is challenging because variations can be introduced to the pattern of a face by varying pose, lighting, scale, and expression. A new face recognition approach using rank correlation of Gabor-filtered images is presented. Using this technique, Gabor filters of different sizes and orientations are applied on images before using rank correlation for matching the face representation. The representation used for each face is computed from the Gabor-filtered images and the original image. Although training requires a fairly substantial length of time, the computation time required for recognition is very short. Recognition rates ranging between 83.5% and 96% are obtained using the AT&T (formerly ORL) database using different permutations of 5 and 9 training images per subject. In addition, the effect of pose variation on the recognition system is systematically determined using images from the UMIST database. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. [References: 40]
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