Abstract
Most face recognition systems are based on some form of batch learning. Online face recognition is not only more practical, it is also much more biologically plausible. Typical batch learners aim at minimizing both training error and (a measure of) hypothesis complexity. We show that the same minimization can be done incrementally as long as some form of ”scaffolding” is applied throughout the learning process. Scaffolding means: make the system learn from samples that are neither too easy nor too difficult at each step. We note that such learning behavior is also biologically plausible. Experiments using large sequences of facial images support the theoretical claims. The proposed method compares well with other, numerical calculus-based online learners.
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References
Aronszajn, N.: Theory of reproducing kernels. Trans. Am. Math. Soc. 68(3), 337–404 (1950)
Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Castrillon, M., Deniz, O., Guerra, C., Hernandez, M.: ENCARA2: Real-time detection of multiple faces at different resolutions in video streams. Journal of Visual Communication and Image Representation (in press, 2007)
Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. In: NIPS, pp. 409–415 (2000)
Chellappa, R., Zhao, W. (eds.): Face Processing: Advanced Modeling and Methods. Elsevier, Amsterdam (2005)
Hall, P., Marshall, D., Martin, R.: Incremental Eigenanalysis for classification. In: Proceedings of the British Machine Vision Conference, vol. 1, pp. 286–295 (1998)
Hofmann, T., Scholkopf, B., Smola, A.J.: A tutorial review of RKHS methods in machine learning (2006), Available at http://sml.nicta.com.au/~smola/papers/unpubHofSchSmo05.pdf
Poggio, T., Smale, S.: The mathematics of learning: Dealing with data. Amer. Math. Soc. Notice 50(5), 537–544 (2003)
Scholkopf, B., Smola, A.: Learning with kernels. MIT Press, Cambridge, MA (2002)
Vanschoenwinkel, B., Manderick, B.: Appropriate kernel functions for support vector machine learning with sequences of symbolic data. In: Deterministic and statistical methods in machine learning (First international workshop), Sheffield, UK, September 2004, pp. 256–280 (2004)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Vygotsky, L.: Mind and society: The development of higher mental processes. Harvard University Press (1978)
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Deniz, O., Lorenzo, J., Castrillon, M., Mendez, J., Falcon, A. (2007). Learning to Recognize Faces Incrementally. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds) Pattern Recognition. DAGM 2007. Lecture Notes in Computer Science, vol 4713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74936-3_37
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DOI: https://doi.org/10.1007/978-3-540-74936-3_37
Publisher Name: Springer, Berlin, Heidelberg
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