Face recognition based on the uncorrelated discriminant transformation

摘要

The extraction of discriminant features is the most fundamental and important problem in face recognition. This paper presents a method to extract optimal discriminant features for face images by using the uncorrelated discriminant transformation and KL expansion. Experiments on the ORL database and the NUST603 database have been performed. Experimental results show that the uncorrelated discriminant transformation is superior to the Foley–Sammon discriminant transformation and the new method to extract uncorrelated discriminant features for face images is very effective. An error rate of 2.5% is obtained with the experiments on the ORL database. An average error rate of 1.2% is obtained with the experiments on the NUST603 database. Experiments show that by extracting uncorrelated discriminant features, face recognition could be performed with higher accuracy on lower than 16×16 resolution mosaic images. It is suggested that for the uncorrelated discriminant transformation, the optimal face image resolution can be regarded as the resolution m×n which makes the dimensionality N=mn of the original image vector space be larger and closer to the number of known-face classes.