Email this article 
Open Access
Subscription or Fee Access
Facial Recognition Using Square Diagonal Matrix Based on Two-Dimensional Linear Discriminant Analysis
(*) Corresponding author
DOI: https://doi.org/10.15866/irecos.v10i7.6623
Abstract
In this research, the square diagonal matrix based on Two-Dimensional Linear Discriminant Analysis for Face recognition is proposed. The original image matrix is converted into the diagonal matrix and followed the same process by using the output become input. The results of the conversion are utilized as input on feature extraction using Two-Dimensional Linear Discriminant Analysis. The proposed method has been evaluated by using the YALE-A, the ORL and the University of Bern Face image databases. The experimental results show that, the proposed method outperformed to other methods, which are Eigen Faces, Fisher Faces, Laplacian Faces and Orthogonal Laplacian Faces methods. The highest recognition rates are 89.523%, 97% and 95.33% for the YALE-A, the ORL, and the University of Bern respectively. The usage of the dominant features has influenced the results of the recognition. It is proved that, the more the dominant features, the smaller error occurred.
Copyright © 2015 Praise Worthy Prize - All rights reserved.
Keywords
References
Marcialis, G., Roli, F. “Fusion of LDA and PCA for Face Recognition.” In Biometric Authentication, Vol. LNCS 2359, pp. 30-37, Jun. 2002.
http://dx.doi.org/10.1007/3-540-47917-1_4
A.M. Martinez, A.C. Kak, “PCA versus LDA.” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 23, No. 2, pp. 228-233, 2001
http://dx.doi.org/10.1109/34.908974
D.Q. Dai and P.C. Yuen, “Regularized Discriminant Analysis and Its Application to Face Recognition,” Pattern Recognition, vol. 36, pp. 845-847, 2003
http://dx.doi.org/10.1016/s0031-3203(02)00092-4
F. Song, S. Liu, and J. Yang. Orthogonalized Fisher Discriminant Pattern Recognition, 38(2):311–313, 2005.
http://dx.doi.org/10.1016/j.patcog.2004.06.007
H. Cevikalp, M. Neamtu, M. Wilkes and A. Barkana, “Discriminative Common Vectors for Face Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 1, pp. 4-13, Jan. 2005.
http://dx.doi.org/10.1109/tpami.2005.9
H. Xiong, M. Swamy, and M. Ahmad. Two-dimensional FLD for face recognition. Pattern Recognition, 38(7):1121–1124, 2005.
http://dx.doi.org/10.1016/j.patcog.2004.12.003
J. Yang and J.Y. Yang, “From Image Vector to Matrix: a Straightforward Image Projection Technique—IMPCA vs. PCA,” Pattern Recognition. vol. 35, no. 9, pp. 1997-1999, 2002.
http://dx.doi.org/10.1016/s0031-3203(02)00040-7
J. Yang, D. Zhang, A.F. Frangi, and J.Y. Yang. Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysisand Machine Intelligence, 26(1):131–137, 2004.
http://dx.doi.org/10.1109/tpami.2004.1261097
L. Wang, X. Wang, X. Zhang and J. Feng, “The Equivalence of Two-Dimensional PCA to Line-Based PCA,” Pattern Recognition. Letter, vol. 26, no. 1, pp. 57-60, 2005.
http://dx.doi.org/10.1016/j.patrec.2004.08.016
Kannan, P., Shantha Selva Kumari, R., A novel approach for face recognition system based on rotational invariant transform and artificial neural networks, (2014) International Review on Computers and Software (IRECOS), 9 (1), pp. 169-177.
Belghini, N., Zarghili, A., 3D face matching based on depth-level curves, (2013) International Review on Computers and Software (IRECOS), 8 (12), pp. 2898-2902.
J. Yang, D. Zhang, X. Yong and J.y. Yang, “Two-dimensional discriminant transform for face recognition,” Pattern Recognition, vol. 38, pp. 1125 – 1129, 2005.
http://dx.doi.org/10.1016/j.patcog.2004.11.019
Sanguansat, P., Asdornwised, W., Jitapunkul, S. & Marukatat, S. Two-dimensional linear discriminant analysis of principle component vectors for face recognition, IEICE Trans. Inf. & Syst. Special Section on Machine Vision Applications E89-D(7): 2164–2170, 2006.
http://dx.doi.org/10.1109/icassp.2006.1660350
X.Y. Jing, H.S., and D. Zhang, "Face recognition based on 2D Fisher face approach," Pattern Recognition, vol. 39, pp. 707-710, 2006.
http://dx.doi.org/10.1016/j.patcog.2005.10.020
L. Wang, X. Wang, and J. Feng, "On Image Matrix Based Feature Extraction Algorithms," IEEE Trans on Systems, Man, and Cybernetics—Part B: Cybernetics, vol. 36, no. 1, PP. 194-197, 2006.
http://dx.doi.org/10.1109/tsmcb.2005.852471
YALE Center for Computational Vision and Control, Yale Face Database, http://cvc.yale.edu/projects/yalefaces/ yalefaces.html
University of Bern, http://www.iam.unibe.ch/fki/databases/iam-faces-database
ORL, Research Center of Att, UK, Olivetti-AttORL FaceDatabase, http://www.uk.research.att.com/faceda base. html.
Refbacks
- There are currently no refbacks.
Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize
