Abstract
Non-negative matrix factorization (NMF) is an unsupervised learning algorithm that can extract parts from visual data. The goal of this technique is to find intuitive basis such that training examples can be faithfully reconstructed using linear combination of basis images which are restricted to non-negative values. Thus NMF basis images can be understood as localized features that correspond better with intuitive notions of parts of images. However, there has not been any systematic study to identify suitable distance measure for using NMF basis images for face recognition.
In this article we evaluate the performance of 17 distance measures between feature vectors based on the result of the NMF algorithm for face recognition. Recognition experiments are performed using the MIT-CBCL database, CMU AMP Face Expression database and YaleB database.
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References
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, New York (1991)
Feng, T., Li, S.Z., Shum, H.-Y., Zhang, H.: Local non-negative matrix factorization as a visual representation. In: ICDL 2002: Proceedings of the 2nd International Conference on Development and Learning, vol. 178, p. 178. IEEE Computer Society, Washington, DC, USA (2002)
Fraser, A., Hengartner, N., Vixie, K., Wohlberg, B.: Incorporating invariants in mahalanobis distance based classifiers: Application to face recognition. In: International Joint Conference on Neural Networks (IJCNN), Portland, OR, USA (2003)
Guillamet, D., Vitrià , J.: Evaluation of distance metrics for recognition based on non-negative matrix factorization. Pattern Recogn. Lett. 24(9-10), 1599–1605 (2003)
Guillamet, D., Vitrià , J.: Non-negative matrix factorization for face recognition. In: Escrig, M.T., Toledo, F.J., Golobardes, E. (eds.) Topics in Artificial Intelligence. LNCS (LNAI), vol. 2504, pp. 336–344. Springer, Heidelberg (2002)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. Adv. Neural Info. Proc. Syst. 13, 556–562 (2001)
Perlibakas, V.: Distance measures for pca-based face recognition. Pattern Recogn. Lett. 25(6), 711–724 (2004)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognitive Neuroscience 3, 71–86 (1991)
Yu, J.: Clustering methods, applications of multivariate statistical analysis. In: Technical report, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871
Zhang, Y., Fang, K.: An Introduction to Multivariate Analysis. Science Press, Beijing (1982)
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Xue, Y., Tong, C.S., Zhang, W. (2007). Survey of Distance Measures for NMF-Based Face Recognition. In: Wang, Y., Cheung, Ym., Liu, H. (eds) Computational Intelligence and Security. CIS 2006. Lecture Notes in Computer Science(), vol 4456. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74377-4_109
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DOI: https://doi.org/10.1007/978-3-540-74377-4_109
Publisher Name: Springer, Berlin, Heidelberg
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