Abstract
A lot of researches on classifiers, which can perform well with a given set of feature vectors, have been done. However, researches on feature vectors, which extract better feature vectors automatically, have not been done very much. We face two problems when we consider feature extraction process. One is how we can make a good feature extractor, and the other is what more separable features are. In this paper, we solved these two problems by proposing feature extractor-training methodology that uses new cluster validity as an objective function. By combining feature extractor to Fuzzy Neural Network Model, we achieve on-line adaptation capability as well as optimized feature extraction. The result shows recognition rate of 97% when on-line adaptation is being done.
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© 2005 Springer-Verlag Berlin Heidelberg
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Lee, S.W., Kim, DJ., Kim, Y.S., Bien, Z. (2005). Training of Feature Extractor via New Cluster Validity – Application to Adaptive Facial Expression Recognition. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11554028_75
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DOI: https://doi.org/10.1007/11554028_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28897-8
Online ISBN: 978-3-540-31997-9
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