Data dimensionality reduction for face recognition

• Autorzy: Kukharev G. , Forczmański P.
• Języki publikacji: EN

Abstrakty

EN

In the process of image recognition in most of the applications there is a problem with gathering, processing and storing large amounts of data. A possible solution for reducing this amounts and speeding--up computations is to use some sort of data reduction. Efficient reduction of the stored data without losing any important part of it requires an adaptive method, which works without any supervision. In this article we discuss a few variants of a two--step approach, which involves Karhunen--Loeve Transform (KLT) and Linear Discriminant Analysis (LDA). The KLT gives a good approximation of the input data, however it requires a large number of eigenvalues. The second step reduces data dimensionality futher using LDA. The efficiency of KLT depends on the quality and quantity of the input data. In the case when only one image in a class is given as input, its features are not stable in comparison with other images in other classes. In this article we present a few methods for solving this problem, which improve on the ideas presented in [6, 9].

Słowa kluczowe

EN

dimensionality reduction; face recognition; eigenvectors; KLT; PCA; LDA

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2004
• Tom: Vol. 13, No. 1/2
• Strony: 99--121
• Opis fizyczny: Bibliogr. 19 poz., il., wykr.

Twórcy

autor

Kukharev G.

• Technical University of Szczecin, Faculty of Computer Science and Information Systems, Żołnierska Str. 49, 71-210 Szczecin

autor

Forczmański P.

• Technical University of Szczecin, Faculty of Computer Science and Information Systems, Żołnierska Str. 49, 71-210 Szczecin

Bibliografia

• [1] AT&T Laboratories Cambridge. The ORL Database of Faces. URL: www.uk.research.att.com/facedatabase.html, 1994.
• [2] Swets D., Weng J. Efficient image retrieval using a network with complex neurons. Proc. Int. Conf. on Neural Networks, Perth, Western Australia, Nov, 1995.
• [3] Swets D. L., Punch B., Weng J. Genetic algorithms for object recognition in a complex scene. ICIP, 2595-2598. URL: citeseer.nj.nec.com/swets95genetic.html, 1995.
• [4] Belhumeur P. N., Hespanha J., Kriegman D. J. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. ECCV, 45-58, 1996.
• [5] Moses Y., Ullman S., Edelman S. Generalization to novel images in upright and inverted faces. URL: citeseer.nj.nec.com/article/moses94generalization.html, 1996.
• [6] Swets D. L., Weng J. Using discriminant eigenfeatures for image retrieval. IEEE Trans. PAMI, 18(8), 831-836, 1996.
• [7] Multimedia - Algorithms and compression standards [in Polish] Skarbek W. (Ed.), Akademicka Oficyna Wydawnicza PLJ, Warszawa, 1998.
• [8] Nefian A., Hayes M. Hidden markov models for face recognition. ICASSP98, 2721-2724. URL: citeseer.nj.nec.com/236258.html, 1998.
• [9] Tsapatsoulis N., Alexopoulos V., Kollias S. A vector based approximation of KLT and its application to face recognition. Proc. of The IX European Signal Processing Conf. EUSIPCO-98. Rhodos Palace, Island of Rhodes, Greece, Sept., 8-11, 1581-1584, 1998.
• [10] Liu C., Wechsler H. Comparative assessment of independent component analysis (ICA) for face recognition. In Proc. the 2nd Int. Conf. on Audioand Video-based Biometric Person Authentication, Washington D. C., March 22-24. URL: citeseer.nj.nec.com/liu99comparative.html, 1999.
• [11] Nefian A., Hayes M. Face recognition using an embedded HMM. Proc. of the IEEE Conf. on Audio and Video-based Biometric Person Authentication, March, 19-24. URL: citeseer.nj.nec.com/nefian99face.html, 1999.
• [12] Swets D. L., Weng J. Hierarchical discriminant analysis for image retrieval. IEEE Trans. on PAMI, 21(5), 386-401. URL: citeseer.nj.nec.com/swets99hierarchical.html, 1999.
• [13] Kuchariew G., Forczmański P.: Compression and arrangement of graphical data for pattern recognition [in Polish]. Proc. of 5th Computer Scientific Session, Jan., 15-21, 2000.
• [14] Kuchariew G., Forczmański P. Hierarchical method of reduction of features dimensionality for image recognition and graphical data retrieval. Proc. of Sixth Int. Conf. PRIP, Minsk, Republic of Belarus, May, 19-34, 2001.
• [15] Kuchariew G., Tujaka A. : Pattern recognition methods for visitor identification and access control. Proc. of Sixth Int. Conf. PRIP, Minsk, Republic of Belarus, May, 19-34, 2001.
• [16] Lee T., Lewicki M. S., Sejnowski T. J. Mixture models for unsupervised classification of non- gaussian classes and automatic context switching in blind signal separation. IEEE Trans. PAMI, 22(10), 1078-1089. ULR: citeseer.nj.nec.com/lee00ica.html, 2001.
• [17] Martinez A. M., Kak A. C. PČA versus LDA. IEEE Trans. on PAMI, 23(2), 228-233. URL: citeseer.nj.nec.com/martinez01pca.html, 2001.
• [18] Havran C., Hupet L., Czyz J., Lee J., Vandendorpe L., Verleysen M. Independent component analysis for face authentication. KES'2002 Proc. Knowledge Based Intelligent Information and Engineering Systems, Crema, Italy, September. URL: citeseer.nj.nec.com/havran02independent.html, 2002.
• [19] Kukharev G., Kuźminski A.: Biometric Techniques, Part 1 - Face Recognition Methods [in Polish]. Faculty of Computer Science and Information Systems, Technical University of Szczecin, 2003.