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| Incremental PCA-LDA Algorithm
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International Journal of Biometrics and Bioinformatics (IJBB) |
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Volume: 4 Issue: 2 |
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Pages: 13-99 |
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Publication
Date: May 2010 |
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ISSN
(Online): 1985-2347 |
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Pages |
86 - 99 |
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Author(s) |
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Published
Date |
10-06-2010 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
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| Keywords Abstract References Cited by Related Articles Collaborative
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KEYWORDS: Recursive PCA-LDA, principal component analysis (PCA, Face recognition |
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| In this paper a recursive algorithm of calculating the discriminant features of the PCA-LDA procedure is introduced. This algorithm computes the principal components of a sequence of vectors incrementally without estimating the covariance matrix (so covariance-free) and at the same time computing the linear discriminant directions along which the classes are well separated. Two major techniques are used sequentially in a real time fashion in order to obtain the most efficient and linearly discriminative components. This procedure is done by merging the runs of two algorithms based on principal component analysis (PCA) and linear discriminant analysis (LDA) running sequentially. This algorithm is applied to face recognition problem. Simulation results on different databases showed high average success rate of this algorithm compared to PCA and LDA algorithms. The advantage of the incremental property of this algorithm compared to the batch PCA-LDA is also shown. |
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| 1 |
H Zhao, PC Yuen, “Incremental Linear Discriminant Analysis for Face Recognition”, Systems, Man, and Cybernetics, Part B, IEEE Transactions on, Vol. 38, No. 1. (2008), pp. 210-221. |
|
|
| 2 |
Shaoning Pang, Seiichi Ozawa, Nikola Kasabov, “Chunk Incremental LDA Computing on Data Streams”, Lecture Notes in Computer Science (Advances in Neural Networks – ISNN 2005), Volume 3497/2005, pp. 51-56 |
|
|
| 3 |
. Ye, H. Xiong Q. Li, H. Park, R. Janardan, and V. Kumar.” IDR/QR: An Incremental Dimension Reduction Algorithm via QR Decomposition”, In Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pages 364–373, 2004. |
|
|
| 4 |
M. Uray, D. Skočaj, P. Roth, H. Bischof and A. Leonardis, “Incremental LDA learning by combining reconstructive and discriminative approaches”, Proceedings of British Machine Vision Conference (BMVC ) 2007, pp. 272-281 |
|
|
| 5 |
Tae-Kyun Kim; Shu-Fai Wong; Stenger, B.; Kittler, J.; Cipolla, R., “Incremental Linear Discriminant Analysis Using Sufficient Spanning Set Approximations” Computer Vision and Pattern Recognition, 2007. IEEE Conference on, Volume , Issue , 17-22 June 2007 pp. 1-8 |
|
|
| 6 |
Haitao Zhao; Pong Chi Yuen; Kwok, J.T.; Jingyu Yang,” Incremental PCA based face Recognition”, ICARCV 2004 Dec. 2004 Page(s): 687 - 691 Vol. 1 |
|
|
| 7 |
Ghassabeh, Y.A.; Ghavami, A.; Moghaddam “A New Incremental Face Recognition System” Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2007. IDAACS 2007. 6-8 Sept. 2007 Page(s):335 – 340 |
|
|
| 8 |
Fengxi Song, David Zhang, Qinglong Chen1, and Jingyu Yang,” A Novel Supervised Dimensionality Reduction Algorithm for Online Image Recognition” , Lecture Notes in Computer Science ; PSIVT 2006, LNCS 4319, pp. 198 – 207, 2006. Springer-Verlag. |
|
|
| 9 |
Fengxi Song , Hang Liu, David Zhang, Jingyu Yang “A highly scalable incremental facial feature extraction method”, Elsevier. Neurocomputing 71 (2008) 1883– 1888 |
|
|
| 10 |
Dagher, I.; Nachar, R. “Face recognition using IPCA-ICA algorithm”, Pattern Analysis and Machine Intelligence, IEEE Transactions on Volume 28, Issue 6, June 2006 Page(s):996 – 1000 |
|
|
| 11 |
B. Raducanu, J. Vitria “Learning to learn: From smart machines to intelligent machines”, Pattern Recognition Letters 29 (2008) 1024–1032 |
|
|
| 12 |
A Haitao Zhao Pong Chi Yuen Kwok, J.T. “novel incremental principal component analysis and its application for face recognition Systems”, Man, and Cybernetics, Part B, IEEE Transactions on. Aug. 2006, Volume: 36, Issue: 4 On page(s): 873-886 |
|
|
| 13 |
J. Karhunen and J. Joutsensalo,” Representation and separation of signals using non linear PCA type learning”, Neural Networks, 7(1),1994 |
|
|
| 14 |
K. Fukunaga, “Introduction to statistical pattern recognition”, Second ed., Academic Press, 1990 |
|
|
| 15 |
J. J. Atick and A. N. Redlich, “What does the retina know about natural scenes?”, Neural Comput., vol. 4, pp. 196-210, 1992. |
|
|
| 16 |
D. L. Swets and J. Weng, “Using discriminant eigenfeatures for image retrieval”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 8, pp. 831–836. August 1996 |
|
|
| 17 |
H. Murase and S.K. Nayar, “Visual Learning and Recognition of 3-D Objects from Appearance,” Int’l J. Computer Vision, vol. 14, no. 1, pp. 5-24, Jan. 1995. |
|
|
| 18 |
Y. Cui and J. Weng, “Appearance-Base Hand Sign Recognition from Intensity Image Sequences,” Computer Vision and Image Understanding, vol. 78, pp. 157-176, 2000. |
|
|
| 19 |
S. Chen and J. Weng, “State-Based SHOSLIF for Indoor Visual Navigation,” IEEE Trans.Neural Networks, vol. 11, no. 6, pp. 1300-1314, 2000. |
|
|
| 20 |
J. Weng and I. Stockman, eds., Proc. NSF/DARPA Workshop Development and Learning, Apr. 2000. |
|
|
| 21 |
A. M. Martinez and A. C. Kak, “PCA versus LDA”, IEEE Trans. Pattern Anal. Mach. Intell.,vol. 23, no. 2, pp. 228–233, Feb 2001 |
|
|
| 22 |
M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991. |
|
|
| 23 |
ORL face database, website: http://www.cam-orl.co.uk/facedatabase.html, AT&T Laboratories Cambridge. |
|
|
| 24 |
UMIST face database, website: http://images.ee.umist.ac.uk/danny/database.html, Daniel Graham. |
|
|
| 25 |
Yale face database,website:http://www1.cs.columbia.edu/~belhumeur/pub/images/yalefaces/, Colubmbia University. |
|
|
| 26 |
Bioid face database, website: www.bioid.com/downloads/facedb/ |
|
|
| 27 |
Yunhong Wang, Tieniu Tan and Yong Zhu, "Face Verification Based on Singular Value Decomposition and Radial Basis Function Neural Network+," Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China, 100080. |
|
|
| 28 |
Hakan Cevikalp, Marian Neamtu, Mitch Wilkes, and Atalay Barkana, "Discriminative Common Vectors for Face Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, pp.6-9, 2005. |
|
|
| 29 |
H. Yu and J. Yang, “A direct LDA algorithm for high-dimensional data—With application to face Recognition” Pattern Recognition, vol. 34, pp. 2067–2070, 2001. |
|
|
| 30 |
P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs Fisherfaces: recognition using class specific linear projection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711–720, 1997. |
|
|
| 31 |
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, Jan 2004. |
|
|
| 32 |
Ye J., Janardan R., and Li Q.,”two dimensional linear discriminant analysis”, NIPS 2004. |
|
|
| 33 |
Xiong H., Swamy M.N.S, and Ahmad M.O.” two dimensional FLD for face recognition” Pattern Recognition, vol 38, pp1121-1124, 2005. |
|
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|
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|
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| 2 |
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|
|
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|
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| Issam Dagher : Colleagues
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