Facial Feature Tracking via Evolutionary Multiobjective Optimization

Eric C. Larson (Oklahoma State University, USA) and Gary G. Yen (Oklahoma State University, USA)

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DOI: 10.4018/jaec.2010010104

Source title: International Journal of Applied Evolutionary Computation (IJAEC)

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MLA

Larson, Eric C. and Gary G. Yen. "Facial Feature Tracking via Evolutionary Multiobjective Optimization." IJAEC 1.1 (2010): 57-71. Web. 18 May. 2013. doi:10.4018/jaec.2010010104

APA

Larson, E. C., & Yen, G. G. (2010). Facial Feature Tracking via Evolutionary Multiobjective Optimization. International Journal of Applied Evolutionary Computation (IJAEC), 1(1), 57-71. doi:10.4018/jaec.2010010104

Chicago

Larson, Eric C. and Gary G. Yen. "Facial Feature Tracking via Evolutionary Multiobjective Optimization," International Journal of Applied Evolutionary Computation (IJAEC) 1 (2010): 1, accessed (May 18, 2013), doi:10.4018/jaec.2010010104

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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.

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