Abstract
In this paper, we propose a novel feature extraction method called Class-Augmented PCA (CA-PCA) which uses class information. The class information is augmented to data and influences the extraction of features so that the features become more appropriate for classification than those from original PCA. Compared to other supervised feature extraction methods LDA and its variants, this scheme does not use the scatter matrix including inversion and therefore it is free from the problems of LDA originated from this matrix inversion. The performance of the proposed scheme is evaluated by experiments using two well-known face database and as a result we can show that the performance of the proposed CA-PCA is superior to those of other methods.
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References
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)
Duda, R., Hard, P.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
Fisher, R.A.: The Use of Multiple Measures in Taxonomic Problems. Annual Eugenics 7, 179–188 (1936)
Huang, R., Liu, Q., Lu, H., Ma, S.: Solving the Small Sample Size Problem of LDA. In: Proceedings of IEEE International Conf. on Pattern Recognition, vol. 3 (2002)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. FisherFaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Yang, J., Yu, Y., Kunz, W.: An Efficient LDA Algorithm for Face Recognition. In: Proceedings of the 6th International Conference on Control, Automation, Robotics and Vision, ICARCV 2000 (2000)
Park, C.H., Park, H., Pardalos, P.: A Comparative Study of Linear and Nonlinear Feature Extraction Methods. In: Proceedings of 4th IEEE International Conference on Data Mining (ICDM 2004), pp. 495–498 (2004)
Yang, M.H.: Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods. In: Proceedings of 5th IEEE Internationl Conference on Automatic Face and Gesture Recognition (RGR 2002), pp. 215–220 (2002)
Yang, J., Zhang, D., Frangi, A.F., Yang, J.-y.: Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 131–137 (2005)
Meng, J.E., Chen, W., Shiqian, W.: Highspeed face recognition based on discrete cosine transform and RBF neural networks. IEEE Transactions on Neural Networks 16(3), 679–691 (2005)
Kwak, N., Choi, C.-H., Ahuja, N.: Face recognition using feature extraction based on independent component analysis. In: Proceedings of International Conference on Image Processing 2002, Rochester (2002)
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© 2006 Springer-Verlag Berlin Heidelberg
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Park, M.S., Na, J.H., Choi, J.Y. (2006). Feature Extraction Using Class-Augmented Principal Component Analysis (CA-PCA). In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_63
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DOI: https://doi.org/10.1007/11840930_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
Online ISBN: 978-3-540-38873-9
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