Open Science Research Excellence

Open Science Index

Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29404


Select areas to restrict search in scientific publication database:
9999412
Quantitative Analysis of PCA, ICA, LDA and SVM in Face Recognition
Abstract:
Face recognition is a technique to automatically identify or verify individuals. It receives great attention in identification, authentication, security and many more applications. Diverse methods had been proposed for this purpose and also a lot of comparative studies were performed. However, researchers could not reach unified conclusion. In this paper, we are reporting an extensive quantitative accuracy analysis of four most widely used face recognition algorithms: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) using AT&T, Sheffield and Bangladeshi people face databases under diverse situations such as illumination, alignment and pose variations.
Digital Object Identifier (DOI):

References:

[1] ToygarOnsen and AcanAdnan (2003): Face recognition using PCA, LDA and ICA approaches on colored images, Journal of electrical and Electronics Engineering vol. 3, No. 1, 735 – 743. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp. 68-73.
[2] Belhumeur P. N., Hespanha J. P. and Kriegman D. J (1997): Eigenfaces vs.Fisherfaces: Recognition Using Class Specific Linear Projection," in IEEE TPAMI. vol. 19, pp. 711-720.K.
[3] Becker B.C. and Ortiz E.G. (2008): Evaluation of Face Recognition Techniques for Application Facebook, in Proceedings of the 8th IEEE International Automatic Face and Gesture, 14 (2008), Carnegie Mellon Univ., Pittsburgh, PA, pp. 1-6.
[4] Delac K., Grgic M., Grgic S. (2002): Independent Comparative Study of PCA, ICA, and LDA on the FERET Data Set, International Journal of Imaging.
[5] Martinez A.M., Kak A.C. (2001): PCA versus LDA, IEEE Trans. Patt. Anal. Mach. Intell. 23 (2) 228–233.6. Mazanec Jan and et al. (2008): Support Vector Machines, PCA and LDA in face recognition, Journal of Electrical engineering, vol. 59, No. 4, pp. 203–209.
[6] Mazanec Jan and et al. (2008): Support Vector Machines, PCA and LDA in face recognition, Journal of Electrical engineering , vol. 59, No. 4, pp. 203–209.
[7] Turk M. A. and Pentland A. P. (1991): Face Recognition Using Eigenfaces, IEEE CVPR 1991, pp. 586-591.
[8] Turk M., Pentland A. (1991): Eigenfaces for Recognition, Journal of Cognitive Neuroscience, Vol. 3, No. 1, 1991, pp. 71-86.
[9] Bartlett M.S., Movellan J.R., and Sejnowski T.J. (2002): Face Recognition by Independent Component Analysis, IEEE Trans. on Neural Networks, Vol. 13, pp. 1450-1464.
[10] Liu C. and Wechsler H. (1999): Comparative Assessment of Independent Component Analysis (ICA) for Face Recognition, Second International Conference on Audio and Videobased Biometric Person Authentication, AVBPA’99,Washington D. C., USA, March 22-24.
[11] Baek K., Draper B., Beveridge J.R. and She K. (2002): PCA vs. ICA: A Comparison on the FERET Data Set, Proc. of the Fourth International Conference on Computer Vision, Pattern Recognition and Image Processing, pp. 824-827.
[12] Moghaddam B. (2002): Principal Manifolds and Probabilistic Subspaces for Visual Recognition, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 24, pp. 780-788,.
[13] Beveridge J.R., She K., Draper B., and Givens G.H. (2001): A Nonparametric Statistical Comparison of PrincipalComponent and Linear Discriminant Subspaces for Face Recognition, Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 535- 542.
[14] Martinez A.M. and Kak A.C (2001): PCA versus LDA, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, pp. 228-233.
[15] Belhumeur V., Hespanha J., and Kriegman D. (1997): Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, pp. 711-720.
[16] Navarrete P. and Ruiz-del-Solar J. (2002): Analysis and Comparison of Eigenspace-Based Face Recognition Approaches, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 16, pp. 817-830.
[17] Belhumeur P., Hespanha J., Kriegman D. (1996): Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection, Proc. of the Fourth European Conference on Computer Vision, Vol. 1, 14-18 April 1996, Cambridge, UK, pp. 45-58.
[18] Zhao W., Chellappa R., Krishnaswamy A. (1998): Discriminant Analysis of Principal Components for Face Recognition, Proc. of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition, 14-16 April 1998, Nara, Japan, pp. 336-341.
[19] Cortes C. and Vapnik V. (1995): Support-vector networks, Machine Learning, Vol.20, No. 3, pp. 273.
[20] Sheffield Face Database,available online: http://www.sheffield.ac.uk/eee/research/iel/research/face
[21] AT&T Face Database,available online: http://www.cl.cam.ac.uk/Research/DTG/attarchive:pub/data/att_faces.zp
Vol:13 No:03 2019Vol:13 No:02 2019Vol:13 No:01 2019
Vol:12 No:12 2018Vol:12 No:11 2018Vol:12 No:10 2018Vol:12 No:09 2018Vol:12 No:08 2018Vol:12 No:07 2018Vol:12 No:06 2018Vol:12 No:05 2018Vol:12 No:04 2018Vol:12 No:03 2018Vol:12 No:02 2018Vol:12 No:01 2018
Vol:11 No:12 2017Vol:11 No:11 2017Vol:11 No:10 2017Vol:11 No:09 2017Vol:11 No:08 2017Vol:11 No:07 2017Vol:11 No:06 2017Vol:11 No:05 2017Vol:11 No:04 2017Vol:11 No:03 2017Vol:11 No:02 2017Vol:11 No:01 2017
Vol:10 No:12 2016Vol:10 No:11 2016Vol:10 No:10 2016Vol:10 No:09 2016Vol:10 No:08 2016Vol:10 No:07 2016Vol:10 No:06 2016Vol:10 No:05 2016Vol:10 No:04 2016Vol:10 No:03 2016Vol:10 No:02 2016Vol:10 No:01 2016
Vol:9 No:12 2015Vol:9 No:11 2015Vol:9 No:10 2015Vol:9 No:09 2015Vol:9 No:08 2015Vol:9 No:07 2015Vol:9 No:06 2015Vol:9 No:05 2015Vol:9 No:04 2015Vol:9 No:03 2015Vol:9 No:02 2015Vol:9 No:01 2015
Vol:8 No:12 2014Vol:8 No:11 2014Vol:8 No:10 2014Vol:8 No:09 2014Vol:8 No:08 2014Vol:8 No:07 2014Vol:8 No:06 2014Vol:8 No:05 2014Vol:8 No:04 2014Vol:8 No:03 2014Vol:8 No:02 2014Vol:8 No:01 2014
Vol:7 No:12 2013Vol:7 No:11 2013Vol:7 No:10 2013Vol:7 No:09 2013Vol:7 No:08 2013Vol:7 No:07 2013Vol:7 No:06 2013Vol:7 No:05 2013Vol:7 No:04 2013Vol:7 No:03 2013Vol:7 No:02 2013Vol:7 No:01 2013
Vol:6 No:12 2012Vol:6 No:11 2012Vol:6 No:10 2012Vol:6 No:09 2012Vol:6 No:08 2012Vol:6 No:07 2012Vol:6 No:06 2012Vol:6 No:05 2012Vol:6 No:04 2012Vol:6 No:03 2012Vol:6 No:02 2012Vol:6 No:01 2012
Vol:5 No:12 2011Vol:5 No:11 2011Vol:5 No:10 2011Vol:5 No:09 2011Vol:5 No:08 2011Vol:5 No:07 2011Vol:5 No:06 2011Vol:5 No:05 2011Vol:5 No:04 2011Vol:5 No:03 2011Vol:5 No:02 2011Vol:5 No:01 2011
Vol:4 No:12 2010Vol:4 No:11 2010Vol:4 No:10 2010Vol:4 No:09 2010Vol:4 No:08 2010Vol:4 No:07 2010Vol:4 No:06 2010Vol:4 No:05 2010Vol:4 No:04 2010Vol:4 No:03 2010Vol:4 No:02 2010Vol:4 No:01 2010
Vol:3 No:12 2009Vol:3 No:11 2009Vol:3 No:10 2009Vol:3 No:09 2009Vol:3 No:08 2009Vol:3 No:07 2009Vol:3 No:06 2009Vol:3 No:05 2009Vol:3 No:04 2009Vol:3 No:03 2009Vol:3 No:02 2009Vol:3 No:01 2009
Vol:2 No:12 2008Vol:2 No:11 2008Vol:2 No:10 2008Vol:2 No:09 2008Vol:2 No:08 2008Vol:2 No:07 2008Vol:2 No:06 2008Vol:2 No:05 2008Vol:2 No:04 2008Vol:2 No:03 2008Vol:2 No:02 2008Vol:2 No:01 2008
Vol:1 No:12 2007Vol:1 No:11 2007Vol:1 No:10 2007Vol:1 No:09 2007Vol:1 No:08 2007Vol:1 No:07 2007Vol:1 No:06 2007Vol:1 No:05 2007Vol:1 No:04 2007Vol:1 No:03 2007Vol:1 No:02 2007Vol:1 No:01 2007