Sparse Bayesian learning in classifying face feature vectors

• Autorzy: Momot A. , Kawulok M.
• Języki publikacji: EN

Abstrakty

EN

The Relevance Vector Machine (RVM), a Bayesian treatment of generalized linear model of identical functional form to the Support Vector Machine (SVM), is the recently developed machine learning framework capable of building simple models from large sets of candidate features. The paper describes the application of the RVM to a classification algorithm of face feature vectors, obtained by Eigenfaces method. Moreover, the results of the RVM classification are compared with those obtained by using both the Support Vector Machine method and the method based on the Euclidean distance.

Słowa kluczowe

EN

supervised learning; Bayesian inference; face recognition

PL

wnioskowanie bayesowskie; rozpoznanie twarzy

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2005
• Tom: Vol. 9
• Strony: 151--158
• Opis fizyczny: Bibliogr. 8 poz., rys., tab.

Twórcy

autor

Momot A.

• Silesian University of Technology, Institute of Computer Science, 16 Akademicka St., 44-101 Gliwice, Poland

autor

Kawulok M.

• Silesian University of Technology, Institute of Computer Science, 16 Akademicka St., 44-101 Gliwice, Poland

Bibliografia

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• [2] MERCER J., Functions of positive and negative type and their connection with the theory of integral equations. Philos. Trans. Roy. Soc. London, A 209, pp. 415 - 446, 1909.
• [3] PHILLIPS P. J, WECHSLER H., HUANG J., AND RAUSS P., “The FERET database and evaluation procedure for face recognition algorithms,” Image and Vision Computing J, Vol. 16, No. 5, pp 295-306, 1998.
• [4] TIPPING M., The Relevance Vector Machine. In Advances in Neural Information Processing Systems 12, pp. 652 - 658, MIT Press, Cambridge, 2000.
• [5] TIPPING M., Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1(2), pp. 211 - 244, 2001.
• [6] TURK M., PENTLAND A., Face Recognition Using Eigenfaces, in: Proceedings of Computer Vision and Pattern Recognition 1991, p.586 – 591.
• [7] VAPNIK V.N., The nature of statistical learning theory. Springer, New York, 1995.
• [8] WIPF D.P., PALMER J.A., RAO B.D., Perspectives on Sparse Bayesian Learning, Neural Information Processing Systems, Vol. 16, pp. 249 – 256, MIT Press, 2004.