MPEG-4 Facial Animation: The Standard,Implementation and Applications
MPEG-4 Facial Animation: The Standard,Implementation and Applications
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Parameterized Models for Facial Animation
IEEE Computer Graphics and Applications
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
An adaptive penalty formulation for constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation
IEEE Transactions on Evolutionary Computation
Rank-density-based multiobjective genetic algorithm and benchmark test function study
IEEE Transactions on Evolutionary Computation
A Generic Framework for Constrained Optimization Using Genetic Algorithms
IEEE Transactions on Evolutionary Computation
Fast and reliable active appearance model search for 3-D face tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Analysis and synthesis of facial image sequences in model-based image coding
IEEE Transactions on Circuits and Systems for Video Technology
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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.