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 It L 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 late of 2.5% ig 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 x 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 x n which makes the dimensionality N = mn of the original image vector space be larger and closer to the number of known-face classes. (C) 2001 pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. [References: 18]
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机译：区分特征的提取是人脸识别中最基本，最重要的问题。本文提出了一种通过不相关判别变换和It L展开来提取人脸图像最佳判别特征的方法。已经在ORL数据库和NUST603数据库上进行了实验。实验结果表明，不相关判别变换优于Foley-Sammon判别变换，提取人脸图像不相关判别特征的新方法非常有效。在ORL数据库上的实验获得了2.5％ig的后期误差。在NUST603数据库上进行的实验得出的平均错误率为1.2％。实验表明，通过提取不相关的判别特征，可以在分辨率低于16 x 16的马赛克图像上以更高的精度进行人脸识别。建议对于不相关的判别变换，可以将最佳的人脸图像分辨率视为分辨率m x n，这使原始图像向量空间的维数N = mn更大并且更接近已知人脸类别的数量。 （C）2001模式识别学会。由Elsevier Science Ltd.出版。保留所有权利。 [参考：18]
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