Training of feature extractor via new cluster validity – application to adaptive facial expression recognition

Authors:
Sang Wan Lee;Dae-Jin Kim;Yong Soo Kim;Zeungnam Bien
Affiliations:
Department of Electrical Engineering and Computer Science, KAIST, Daejeon, Korea;Human-friendly Welfare Robot System Engineering Research Center, KAIST, Daejeon, Korea;Division of Computer Engineering, Daejeon University, Daejeon, Korea;Department of Electrical Engineering and Computer Science, KAIST, Daejeon, Korea
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Year:
2005

Citing 4
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Simultaneous feature selection and classifier training via linear programming: a case study for face expression recognition

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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.