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Quantitative Analysis on Robustness of FLD and PCA-based Face Recognition Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 99 - Number 19 |
| Year of Publication: 2014 |
| Authors: Mahbuba Begum, Md. Golam Moazzam, Mohammad Shorif Uddin |
10.5120/17480-8222
|
Mahbuba Begum, Md. Golam Moazzam, Mohammad Shorif Uddin . Quantitative Analysis on Robustness of FLD and PCA-based Face Recognition Algorithms. International Journal of Computer Applications. 99, 19 ( August 2014), 10-14. DOI=10.5120/17480-8222
Abstract
Principal Component Analysis (PCA) has emerged as a more efficient approach for extracting features for many pattern classification problems. It has been the standard approach to reduce the high-dimensional original pattern vector space into low-dimensional feature vector space, that removes some of the noisy directions. PCA is an unsupervised technique which does not include label information of the data. In addition to PCA, another method Fisher Linear Discriminant (FLD) analysis has been widely used. In this paper, we report experimental results to quantify the robustness of PCA and FLD methods for face recognition. The experimentation was performed based on different levels of additive noise and rotations in handling face recognition problem. FLD outperforms the traditional PCA on the basis of robustness.
References
- H. A. Rowley, S. Beluja, T. Kanade, "Neural Network-Based Face Detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, pp. 23-38, Jan. 1998.
- R. Hsu. M. Mottleb, A. K. Jain, "Face Detection in Color Images", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5, May 2002.
- Y. Yokoo, M. Hagiwara, "Human Faces Detection Method using Genetic Algorithm", in Proc. IEEE Int. Conf. on Evolutionary Computation, pp. 113-118, May 1996.
- G. Yang, T. S. Huang, "Human Face Detection in a Complex Background", Pattern Recognition, Vol. 27, No. 1, pp. 53-63, 1994.
- V. Perlibakas, "Face Recognition using Principal Component Analysis and Wavelet Packet Decomposition", Informatica, Vol. 15, No. 2, pp. 243- 250, 2004.
- K. Kim, "Face Recognition using Principal Component Analysis", National Institute of Technology, Rourkela, India, pp. 89-97, 2008.
- V. Perlibakas, "Distance Measures for PCA-based Face Recognition", Pattern Recognition Letters, Vol. 25, No. 6, pp. 711-724, April 2004.
- M. Turk, A. Pentland, "Face Recognition using Eigenfaces", Conference on Computer Vision and Pattern Recognition, 3 – 6 June 1991, Maui, HI , USA, pp. 586-591.
- H. Kong, L. Wang and E. K. Teoh, J. G. Wang and V. Ronda, "A Framework of 2D Fisher Discriminant Analysis: Application to Face Recognition with Small Number of Training Samples", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1083-1088, 2005.
- Harmon, L. D. , "The recognition of faces", Scientific American, Vol. 5, pp. 71-82, 1973.
- P. N. Belhumeur, J. Hespanda, and D. Kiregeman, "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection", IEEE Trans. on PAMI, Vol. 19, No. 7, pp. 711-720, July 1999.
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