Normalization Discriminant Independent Component Analysis
In face recognition, feature extraction techniques attempts to search for appropriate representation of the data. However, when the feature dimension is larger than the samples size, it brings performance degradation. Hence, we propose a method called Normalization Discriminant Independent Component Analysis (NDICA). The input data will be regularized to obtain the most reliable features from the data and processed using Independent Component Analysis (ICA). The proposed method is evaluated on three face databases, Olivetti Research Ltd (ORL), Face Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC). NDICA showed it effectiveness compared with other unsupervised and supervised techniques.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1086685Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1712
 M. A. Turk and A. P. Pentland, “Face Recognition Using Eigenfaces”, IEEE Conf. on Computer Visionand Pattern Recognition, pp. 586-591, 1991
 Chelali, F.Z.; Djeradi, A.; Djeradi, R.,"Linear discriminant analysis for face recognition," Multimedia Computing and Systems, 2009. ICMCS '09. International Conference on , vol., no., pp.1,10, 2-4 April 2009
 M. S. Bartlett, H. M. Lades, and T. J. Sejnowski, "Independent component representations for face recognition," presented at SPIE Symposium on Electronic Imaging: Science and Technology; Conference on Human Vision and Electronic Imaging III, San Jose, CA, 1998.
 Ying-Han Pang, Andrew Teoh Beng Jin, David Ngo Chek Ling, “Face authentication system using pseudo Zernike moments on wavelet subband”. Proc. IEICE Electr. Exp. v1 i10. 275-280
 Wiskott, L.; Fellous, J.-M.; Kruger, Norbert; Von der Malsburg, C., "Face recognition by elastic bunch graph matching," Image Processing, 1997. Proceedings., International Conference on , vol.1, no., pp.129,132 vol.1, 26-29 Oct 1997
 I. Sirovich and M. Kirby, “Low- dimensional procedure for the caracterization of human faces,”Journal of Optical Society of America A, vol. 4, no. 3, pp. 519–524, March 1987.
 Peter N. Belhumeur, J. P. Hespanha, David J. Kriegman, Eigenfaces vs. Fishefaces: Recognition Using Class Specific Linear Projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.19 n.7, p.711-720, July 1997.
 Shiladitya Chowdhury, Jamuna Kanta Sing, Dipak Kumar Basu, Mita Nasipuri, Face recognition by generalized two dimensional FLD method and multi-class support vector machines, Appl. Soft Comput. J. (2011)
 Keun-Chang Kwak; Pedrycz, W., "Face Recognition Using an Enhanced Independent Component Analysis Approach," Neural Networks, IEEE Transactions on , vol.18, no.2, pp.530,541, March 2007
 Dagher, I.; Nachar, R., "Face recognition using IPCA-ICA algorithm," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.28, no.6, pp.996,1000, June 2006
 O. Deniz, M. Castrillon, M. Hernandez, “Face recognition using independent component analysis and support vector machines”, Pattern Recognition Letters, v.24 n.13, p.2153 – 2157, September 2003
 Dao-Qing Dai; Yuen, P.C., "Face Recognition by Regularized Discriminant Analysis," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on , vol.37, no.4, pp.1080,1085, Aug. 2007
 J. W. Lu , K. N. Plataniotis and A. N. Venetsanopoulos "Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition", Pattern Recognit. Lett., vol. 26, no. 2, pp.181 -191 2005.