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Enhanced Face Recognition based on PCA and SVM
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 117 - Number 2 |
| Year of Publication: 2015 |
| Authors: K.venkata Narayana, V.v.r. Manoj, K.swathi |
10.5120/20530-2871
|
K.venkata Narayana, V.v.r. Manoj, K.swathi . Enhanced Face Recognition based on PCA and SVM. International Journal of Computer Applications. 117, 2 ( May 2015), 40-42. DOI=10.5120/20530-2871
Abstract
Feature Extraction and classification are important aspects of pattern recognition, computer vision. Principal Component Analysis is a well-known feature extraction and data representation technique. But this method is affected by illumination conditions. The combination o PCA an SVM for face recognition is presented in this paper. Before applying Principal Component Analysis preprocessing o images done by using wavelet transform. After PCA is applied or feature extraction. Support Vector Machine is used or classification. Experiments based on using Indian face database. The new technique achieves better performance than using PCA only.
References
- R. Chellappa, C. L. Wilson, S. Sirohey, "Human and MachineRecognition of Faces: A Survey," Proceedings of the IEEE, Vol. 83, No. 5, May. 1995, pp. 705-740.
- S. Ranganath and K. Arun, "Face Recognition Using Transform Features and Neural Network," Pattern Recognition, Vol. . 30, Oct. 1997,pp. 1615-1622.
- Adini, Y. , Moses, Y. , Ullman, S. , "Face Recognition: The Problem of Compensating for Changes in Illumination Direction," IEEE Transactions Vol. 19, No. 7, Jun. 1997,pp. 721-732,.
- G. Guodong, S. Li, and C. Kapluk. "Face recognition by support vector machines," In Proc. IEEE International Conference on Automatic Face and Gesture Recognition, Mar. 2000,pp. 196–201.
- E. Osuna, R. Freund, and F. Girosi, "Training support vector machines: An application to face detection," Proc. Computer Visionand Pattern Recognition,Vol. 3, Jun. 1997,pp. 130–136.
- M. Kirby, L. Sirovich, "Application of Karhunen–Loeve procedure for the characterization of human faces", IEEE Transcvactions on Pattern Analysis and Machine Intelligence vol. 12, pp. 103–108, 1990.
- M. Turk and A. Pentland, "Eigenfaces for Recognition," J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
- Yang J, Zhang D, Frangi A. F. , and Yang J. Y. "Two dimensional PCA: A new approach to appearance-based face representation and recognition", IEEE PAMI, vol. 26, no 1 pp. 131-137, 2004.
- MULLER, K. |MIKA, S. |RATSCH, G. |TSUDA, K. | SCHOLKOPF, B. : An Introduction to Kernel-Based Learning Algorithms, IEEE Transactions on Neural Networks 12 No. 2 (March 2001), 181-201.
- ORAVEC, M. |BESZ¶EDE•S, M. |ROZINAJ, G. : Detection and Recognition of Human Faces and Facial Features, book chapter in Speech, Audio, Image and Biomedical Signal Processing Using Neural Networks" (Bhanu Prasad and S. R. Mahadeva Prasanna, eds. ), Springer-Verlag, Germany, 2008,pp. 283-306
- VAPNIKV. N. : The Nature of Statistical Learning Theory,Springer, 1995
- HSU, C. W. |CHANG, C. C. |LIN, C. J. : A Practical Guide to Support Vector Classification, 2008http://www. csie. ntu. edu. tw/~cjlin/papers/guide/guide. pdf.
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