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Facial Feature Tracking via Evolutionary Multiobjective Optimization

Author

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  • Eric C. Larson

    (Oklahoma State University, USA)

  • Gary G. Yen

    (Oklahoma State University, USA)

Abstract

Facial feature tracking for model–based coding has evolved over the past decades. Of particular interest is its application in very low bit rate coding in which optimization is used to analyze head and shoulder sequences. We present the results of a computational experiment in which we apply a combination of non-dominated sorting genetic algorithm and a deterministic search to find optimal facial animation parameters at many bandwidths simultaneously. As objective functions are concerned, peak signal-to-noise ratio is maximized while the total number of facial animation parameters is minimized. Particularly, the algorithm is tested for efficiency and reliability. The results show that the overall methodology works effectively, but that a better error assessment function is needed for future study.

Suggested Citation

  • Eric C. Larson & Gary G. Yen, 2010. "Facial Feature Tracking via Evolutionary Multiobjective Optimization," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 1(1), pages 57-71, January.
    • Handle: RePEc:igg:jaec00:v:1:y:2010:i:1:p:57-71
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    Cited by:

    1. Deepak Gaur & Deepti Mehrotra & Karan Singh, 2020. "Image correlation method to simulate physical characteristic of particulate matter," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 400-410, April.

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