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Training of Feature Extractor via New Cluster Validity – Application to Adaptive Facial Expression Recognition

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3684))

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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.

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References

  1. Park, G.-T.: A Study on Extraction of Emotion from Facial Image using Soft Computing Techniques. Ph.D Thesis, Dept. of Electrical Engineering and Computer Science, KAIST (1998)

    Google Scholar 

  2. Kapoor, A., Yuan, Q., Picard, R.W.: Fully Automatic Upper Facial Action Recognition. IEEE International Analysis and Modeling of Faces and Gestures, 195–202 (2003)

    Google Scholar 

  3. Fasel, B.: Robust Facial Analysis using Convolutional Neural Networks. In: Proc. of the ICPR (2002)

    Google Scholar 

  4. Guodong, G., Charles, R.D.: Simultaneuous Feature Selection and Classifier Training via Linear Programming: A Case Study for Face Expression Recognition. In: Proc. Of IEEE CVPR (2003)

    Google Scholar 

  5. Cohen, I., et al.: Facial Expression Recognition From Video Sequences. In: ICME (2002)

    Google Scholar 

  6. Bezdek, J.C.: Numerical taxonomy with fuzzy sets. J. Math. Biol. 1, 57–71 (1974)

    Article  MATH  MathSciNet  Google Scholar 

  7. Xuanli, L., Beni, G.: A Validity Measure for Fuzzy Clustering. IEEE Trans. on PAMI 13 (1991)

    Google Scholar 

  8. Kim, D.-W., Lee, K.H., Lee, D.H.: Fuzzy Cluster Validation Index based on Inter-Cluster Proximity. Pattern Recognition Letters (2003)

    Google Scholar 

  9. Kim, Y.S., Ham, C.H., Baek, Y.S.: A Fuzzy Neural Network Model Solving the Underutilization Problem. Journal of Korea Fuzzy Logic and Intelligent Systems Society 11, 354–358 (2001)

    Google Scholar 

  10. Lee, S.W., Kim, D.-J., Kim, Y.S., Bien, Z.: An Adaptive Facial Expression Recognition System Using Fuzzy Neural Network Model and Q-learning. In: SCISISIS, Yokohama. Japan (2004)

    Google Scholar 

  11. http://www.cs.bham.ac.uk/resources/courses/robotics/doc/opencvdocs/ref/OpenCVRef_Experimental.htm#decl_cvGetHaarClassifierCascadeScale

  12. Ranganathan, A.: The LM Algorithm, Report of BORG Lab. in Georgia Institute of Technology (2004)

    Google Scholar 

  13. Edwin, K.P., Zak, S.H.: An Introduction to Optimization. John Wiley & Sons, Chichester (2001)

    MATH  Google Scholar 

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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

  • eBook Packages: Computer ScienceComputer Science (R0)

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