Face Recognition Using Fisher Linear Discriminant Analysis and Support Vector Machine
A new face recognition method is presented based on Fisher’s Linear Discriminant Analysis (FLDA) and Support Vector Machine (SVM). The FLDA projects the high dimensional image space into a relatively low-dimensional space to acquire most discriminant features among the different classes. Recently, SVM has been used as a new technique for pattern classification and recognition. We have used SVM as a classifier, which classifies the face images based on the extracted features. We have tested the potential of SVM on the ORL face database. The experimental results show that the proposed method provides higher recognition rates compared to some other existing methods.
KeywordsFisher’s Linear Discriminant Analysis (FLDA) Support Vector Machine (SVM)
Unable to display preview. Download preview PDF.
- 5.Chellapa, R., Wilson, C., Sirohey, S.: Human and machine recognition of faces: a survey. J. IEEE 83(5), 705–741 (1995)Google Scholar
- 6.ORL face database. AT&T Laboratories, Cambridge, U. K., http://www.uk.research.att.com/facedatabase.html
- 8.Pan, Y.Q., Liu, Y., Zheng, Y.W.: Face recognition using kernel PCA and hybrid flexible neural tree. In: International Conference on Wavelet Analysis and Pattern Recognition, China, pp. 1361–1366 (2007)Google Scholar
- 9.Thakur, S., Sing, J.K., Basu, D.K., Nasipuri, M.: Face recognition by integrating RBF neural networks and a distance measure. In: International Conference on Computer,Communication, Control and Information Technology, India, pp. 264–269 (2009)Google Scholar
- 10.Vapnik, V.N.: Statistical learning theory. John Wiley & Sons, New York (1998)Google Scholar
- 11.Wang, L., Sun, Y.: A new approach for face recognition based on SGFS and SVM. Proc. IEEE, 527–530 (2007)Google Scholar