Abstract
Principle Component Analysis (PCA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in the traditional PCA some weaknesses. In this paper, we propose a new PCA-based method that can overcome one drawback existed in the traditional PCA method. In face recognition where the training data are labeled, a projection is often required to emphasize the discrimination between the clusters. PCA may fail to accomplish this, no matter how easy the task is, as they are unsupervised techniques. The directions that maximize the scatter of the data might not be as adequate to discriminate between clusters. So we proposed a new PCA-based scheme which can straightforwardly take into consideration data labeling, and makes the performance of recognition system better. Experiment results show our method achieves better performance in comparison with the traditional PCA method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Sirovich, L., Kirb, M.: Low-Dimensional Procedure for Characterization of Human Faces. J. Optical Soc. Am. 4, 519–524 (1987)
Kirby, M., Sirovich, L.: Application of the KL Procedure for the Characterization of Human Faces. IEEE Trans. Pattern Analysis and Machine Intelligence 12, 103–108 (1990)
Turk, M., Pentland, A.: Eigenfaces for Recognition. J. Cognitive Neuroscience 3, 71–86 (1991)
Pentland, A.: Looking at People: Sensing for Ubiquitous and Wearable Computing. IEEE Trans. Pattern Analysis and Machine Intelligence 22, 107–119 (2000)
Grudin, M.A.: On Internal Representations in Face Recognition Systems. Pattern Recognition 33, 1161–1177 (2000)
Cottrell, G.W., Fleming, M.K.: Face Recognition Using Unsupervised Feature Extraction. In: Proc. Intl. Neural Network Conf., pp. 322–325 (1990)
Valentin, D., Abdi, H., O.Toole, A.J., Cottrell, G.W.: Connectionist Models of Face Processing: A Survey. Pattern Recognition 27, 1209–1230 (1994)
Penev, P.S., Sirovich, L.: The Global Dimensionality of Face Space. In: Proc. Fourth IEEE Int.l Conf. Automatic Face and Gesture Recognition, pp. 264–270 (2000)
Zhao, L., Yang, Y.: Theoretical Analysis of Illumination in PCA-Based Vision Systems. Pattern Recognition 32, 547–564 (1999)
Wiskott, L., Fellous, J.M., Kruger, N., Von Der Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 775–779 (1997)
Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face Recognition by Independent Component Analysis. IEEE Trans. Neural Networks 13, 1450–1464 (2002)
Draper, B.A., Baek, K., Bartlett, M.S., Beveridge, J.R.: Recognizing Faces with PCA and ICA. Computer Vision and Image Understanding: Special Issue on Face Recognition
Yuen, P.C., Lai, J.H.: Face Representation Using Independent Component Analysis. Pattern Recognition 35, 1247–1257 (2002)
Yang, M.H.: Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods. In: Proc Fifth IEEE Int.l Conf. Automatic Face and Gesture Recognition (RGR 2002), pp. 215–220 (2002)
Koren, Y., Carmel, L.: Robust linear dimensionality reduction. Visualization and Computer Graphics. IEEE Transactions 10, 459–470 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nhat, V.D.M., Lee, S. (2005). An Improvement on PCA Algorithm for Face Recognition. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_163
Download citation
DOI: https://doi.org/10.1007/11427391_163
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
eBook Packages: Computer ScienceComputer Science (R0)