Abstract
In this paper, we propose face feature selection and classifier selection method for face image group according illuminant. In knowledge based, we stored context and weight for feature points and selected classifier for context. This context is distinguished the face images having varying illumination. This context knowledge can be accumulated and used later. Therefore we designed the face recognition system by using evolution method and efficient face feature point selection. It can improve its performance incrementally using proposed algorithm. And we proposed efficient context modeling method by using SOM. For context awareness, we made artificial face images from FERET fa dataset and divided several group. Therefore we improved face recognition ratio using adaptable classifier, feature and weight for feature points.
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© 2006 Springer-Verlag Berlin Heidelberg
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Nam, M.Y., Rhee, P.K. (2006). Pattern Recognition Using Evolutionary Classifier and Feature Selection. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_45
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DOI: https://doi.org/10.1007/11881599_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
Online ISBN: 978-3-540-45917-0
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