×

zbMATH — the first resource for mathematics

Face recognition based on the uncorrelated discriminant transformation. (English) Zbl 0978.68118
Summary: 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 \(16\times 16\) resolution mosaic images. It is suggested that for the uncorrelated discriminant transformation, the optimal face image resolution can be regarded as the resolution \(m\times n\) which makes the dimensionality \(N= mn\) of the original image vector space be larger and closer to the number of known-face classes.

MSC:
68T10 Pattern recognition, speech recognition
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] Samal, A.; Iyengar, P.A., Automatic recognition and analysis of human faces and facial expressions: a survey, Pattern recognition, 25, 1, 65-77, (1992)
[2] Chellappa, R.; Wilson, C.L.; Sirohey, S., Human and machine recognition of faces: A survey, Proc. IEEE, 83, 5, 705-740, (1995)
[3] Fisher, R.A., The use of multiple measurements in taxonomic problems, Ann. eugenics, 7, 178-188, (1936)
[4] Foley, D.H.; Sammon, J.W., An optimal set of discriminant vectors, IEEE trans. comput., 24, 3, 281-289, (1975) · Zbl 0296.68106
[5] Duchene, J.; Leclercq, S., An optimal transformation for discriminant and principal component analysis, IEEE trans. pattern anal. Mach. intell., 10, 6, 978-983, (1988) · Zbl 0655.62064
[6] Jin, Z.; Yang, J.-Y.; Lu, J.-F., An optimal set of uncorrelated discriminant features, Chin. J. comput., 22, 10, 1105-1108, (1999), (in Chinese)
[7] Hong, Z.Q.; Yang, J.Y., Optimal discriminant plane for a small number of samples and design method of classifier on the plane, Pattern recognition, 24, 4, 317-324, (1991)
[8] Liu, K.; Yang, J.Y., An efficient algorithm for Foley-Sammon optimal set of discriminant vectors by algebraic method, Int. J. pattern recognition artif. intell., 6, 5, 817-829, (1992)
[9] Hughes, G.F., On the Mean accuracy of statistical pattern recognizers, IEEE trans. inform. theory, 14, 1, 55-63, (1968)
[10] Fukunaga, K., Introduction to statistical pattern recognition, (1990), Academic Press New York · Zbl 0711.62052
[11] Jin, Z.; Hu, Z.-S.; Yang, J.-Y., A face recognition method based on the BP neural network, J. comput. res. dev., 36, 3, 274-277, (1999), (in Chinese)
[12] Woods, K., Combination of multiple classifiers using local accuracy estimates, IEEE trans. pattern anal. Mach. intell., 19, 4, 405-410, (1997)
[13] Gutta, S.; Wechsler, H., Face recognition using hybrid classifiers, Pattern recognition, 30, 4, 539-553, (1997)
[14] Kittler, J., On combining classifiers, IEEE trans, Pattern anal. Mach. intell., 20, 3, 226-239, (1998)
[15] Lawrence, S.; Giles, C.L.; Tsoi, A.C.; Back, A.D., Face recognition: a convolutional neural-network approach, IEEE trans. neural network, 8, 1, 98-113, (1997)
[16] Z. Jin, Research on feature extraction of face images and feature dimensionality, Ph.D. Dissertation, Nanjing University of Science and Technology, June 1999.
[17] Harmon, L.D., The recognition of faces, Sci. am., 229, 71-82, (1973)
[18] Tamura, S., Male/female identification from 8×6 very low resolution face images by neural network, Pattern recognition, 29, 2, 331-335, (1996)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.