Facial feature analysis in dynamic bandwidth environments: A genetic approach

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 (NSGA-II) 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 chosen to be maximized while the total number of facial animation parameters is chosen to be 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.