Abstract
Principal component analysis (PCA) and Linear Discriminant Analysis (LDA) techniques are among the most common feature extraction techniques used for the recognition of faces. In this paper, two face recognition systems, one based on the PCA followed by a feedforward neural network (FFNN) called PCA-NN, and the other based on LDA followed by a FFNN called LDA-NN, are developed. The two systems consist of two phases which are the PCA or LDA preprocessing phase, and the neural network classification phase. The proposed systems show improvement on the recognition rates over the conventional LDA and PCA face recognition systems that use Euclidean Distance based classifier. Additionally, the recognition performance of LDA-NN is higher than the PCA-NN among the proposed systems.
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Chellappa, R., Wilson, C.L.: Human and machine recognition of faces: A survey. Proc. of IEEE 83 5, 705–741 (1995)
Carey, S., Diamond, R.: From Piecemeal to Configurational Representation of Faces. Science 195, 312–313 (1977)
Bledsoe, W.W.: The Model Method in Facial Recognition, Panoramic Research Inc., Palo Alto, CA, Rep. PRI:15 (1966)
Bledsoe, W.W.: Man-Machine Facial Recognition, Panoramic Research Inc., Palo Alto, CA, Rep. PRI:22 (1966)
Fischler, M.A., Elschlager, R.A.: The Representation and Matching of Pictorial Structures. IEEE Trans. on Computers, c-22.1 (1973)
Harmon, L.D., Hunt, W.F.: Automatic Recognition of Human Face Profiles. Computer Graphics and Image Processing 6, 135–156 (1977)
Kaufman, G.J., Breeding, K.J.: The Automatic Recognition of Human Faces From Profile Silhouettes. IEEE Trans. Syst. Man Cybern. 6, 113–120 (1976)
Kirby, M., Sirovich, L.: Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces. IEEE PAMI 12, 103–108 (1990)
Sirovich, L., Kirby, M.: Low-Dimensional Procedure for the Characterization of Human Faces. J. Opt. Soc. Am. A 4(3), 519–524 (1987)
Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)
Pentland, A., Moghaddam, B., Starner, T.: Viewbased and modular eigenspaces for face recognition. In: Proceedings of the 1994 Conference on Computer Vision and Pattern Recognition, Seattle, WA, pp. 84–91. IEEE Computer Society, Los Alamitos (1994)
Moghaddam, B., Pentland, A.: Probabilistic visual learning for object recognition. PAMI 9(7), 696–710 (1997)
Belhumeur, P., Hespanha, J., Kriegman, D.: Using discriminant eigenfeatures for image retrieval. PAMI 19(7), 711–720 (1997)
Zhao, W., Chellappa, R., Nandhakumar, N.: Empirical performance analysis of linear discriminant classifiers. In: Proc. Computer Vision and Pattern Recognition, Santa Barbara, CA, pp. 164–169 (1998)
Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face Recognition: A Convolutional Neural-Network Approach. IEEE Trans. Neural Networks 8(1), 98–113 (1997)
Zhujie, Yu, Y.L.: Face Recognition with Eigenfaces. In: Proc. of the IEEE Intl. Conf., pp. 434–438 (1994)
Eleyan, A., Demirel, H.: Face Recognition System based on PCA and Feedforward Neural Networks. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 935–942. Springer, Heidelberg (2005)
AT & T Laboratories Cambridge. The ORL Database of faces, http://www.cam-orl.co.uk/facedatabase.html
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Eleyan, A., Demirel, H. (2006). PCA and LDA Based Face Recognition Using Feedforward Neural Network Classifier. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11848035_28
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DOI: https://doi.org/10.1007/11848035_28
Publisher Name: Springer, Berlin, Heidelberg
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