Facial Expression Synthesis for a Desired Degree of Emotion Using Fuzzy Abduction

  • Sumantra Chakraborty
  • Sudipta Ghosh
  • Amit Konar
  • Saswata Das
  • Ramadoss Janarthanan
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)


Modulating facial expression of a subject to exhibit emotional content is an interesting subject of current research in Human-Computer Interactions. The ultimate aim of this research is to exhibit the emotion-changes of the computer on the monitor as a reaction to subjective input. If recognizing emotion from a given facial expression (of a subject) is referred to as forward (deductive) reasoning, the present problem may be considered as abduction. The logic of fuzzy sets has widely been used in the literature to reason under uncertainty. The present problem of abduction includes different sources of uncertainty, including inexact appearance in facial expression to describe a given degree of a specific emotion, noisy ambience and lack of specificity in features. The logic of fuzzy sets, which has proved itself successful to handle uncertainty in abduction, thus can be directly employed to handle the present problem. Experiments undertaken reveal that the proposed approach is capable of producing emotion-carrying facial expressions of desired degrees. The visual examination by subjective experts confirms that the produced emotional expressions lie within given degrees of emotion-carrying expressions.


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  1. 1.
    Arnould, T., et al.: Backward-chaining with fuzzy “if... then...” rules. In: Proc. 2nd IEEE Inter. Conf. Fuzzy Systems, pp. 548–553 (1993)Google Scholar
  2. 2.
    Arnould, T., Tano, S.: Interval-valued fuzzy backward reasoning. IEEE Trans. Fuzzy Systems 3(4), 425–437 (1995)CrossRefGoogle Scholar
  3. 3.
    El Ayeb, B., et al.: A New Diagnosis Approach by Deduction and Abduction. In: Proc. Int’l Workshop Expert Systems in Eng. (1990)Google Scholar
  4. 4.
    Bhatnagar, R., Kanal, L.N.: Structural and Probabilistic Knowledge for Abductive Reasoning. IEEE Trans. on Pattern Analysis and machine Intelligence 15(3), 233–245 (1993)CrossRefGoogle Scholar
  5. 5.
    Bylander, T., et al.: The computational complexity of abduction. Artificial Intelligence 49, 25–60 (1991)CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    de Campos, L.M., Gámez, J.A., Moral, S.: Partial Abductive Inference in Bayesian Belief Networks—An Evolutionary Computation Approach by Using Problem-Specific Genetic Operators. IEEE Transactions on Evolutionary Computation 6(2) (April 2002)Google Scholar
  7. 7.
    Chakraborty, S., Konar, A., Jain, L.C.: An efficient algorithm to computinh Max-Min inverse fuzzy relation for Abductive reasoning. IEEE Trans. on SMC-A, 158–169 (January 2010)Google Scholar
  8. 8.
    Charniak, E., Shimony, S.E.: Probalilistic Semantics for Cost Based Abduction. In: Proc., AAAI 1990, pp. 106–111 (1990)Google Scholar
  9. 9.
    Hobbs, J.R.: An Integrated Abductive Framework for Discourse Interpretation. In: Proceedings of the Spring Symposium on Abduction, Stanford, California (March 1990)Google Scholar
  10. 10.
    Pedrycz, W.: Inverse Problem in Fuzzy Relational Equations. Fuzzy Sets and Systems 36, 277–291 (1990)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Peng, Y., Reggia, J.A.: Abductive Inference Models for Diagnostic Problem-Solving. Springer-Verlag New York Inc. (1990)Google Scholar
  12. 12.
    Saha, P., Konar, A.: A heuristic algorithm for computing the max-min inverse fuzzy relation. Int. J. of Approximate Reasoning 30, 131–147 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Yamada, K., Mukaidono, M.: Fuzzy Abduction Based on Lukasiewicz Infinite-valued Logic and Its Approximate Solutions. In: FUZZ IEEE/IFES, pp. 343–350 (March 1995)Google Scholar
  14. 14.
    Petrantonakis, P.C., Hadjileontiadis, L.J.: Emotion Recognition from EEG Using Higher Order Crossings. IEEE Transactions on Information Technology in Biomedicine 14(2) (March 2010)Google Scholar
  15. 15.
    Klir, G.J., Yuan, B.: Approximate reasoning: Fuzzy sets and fuzzy Logic (2002)Google Scholar
  16. 16.
    Chakraborty, A., Konar, A., Pal, N.R., Jain, L.C.: Extending the Contraposition Property of Propositional Logic for Fuzzy Abduction. IEEE Transaction on Fuzzy Systems 21, 719–734 (2013)CrossRefGoogle Scholar
  17. 17.
    Konar, A.: Artificial Intelligence and Soft Computing. CRC Press LLC (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sumantra Chakraborty
    • 1
  • Sudipta Ghosh
    • 1
  • Amit Konar
    • 1
  • Saswata Das
    • 1
  • Ramadoss Janarthanan
    • 2
  1. 1.Electronics and Tele-Communication Engineering DepartmentJadavpur UniversityKolkataIndia
  2. 2.Computer Science and Engineering DepartmentTJS Engineering CollegeChennaiIndia

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