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Facial modeling from an uncalibrated face image using flexible generic parameterized facial models

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2 Author(s)
Shinn-Ying Ho ; Dept. of Inf. Eng., Feng Chia Univ., Taichung, Taiwan ; Hui-Ling Huang

The paper presents an optimization approach for facial modeling from an uncalibrated face image using flexible generic parameterized facial models (FGPFMs). An FGPFM consists of a topological structure and geometric knowledge of human faces. The topological description consists of a set of well-designed triangular polygons with a multilayered elastic structure in which the microstructural information can be expressed without complicated facial features. All the geometric values are obtained from a set of training facial models using statistical approaches and genetic algorithms. FGPFM can be easily modified using facial features as FGPFMs parameters to create an accurate specific three-dimensional (3D) facial model from only a photograph of an individual with a yawed face. In addition, the facial modeling problem is formulated as a parameter optimization problem. A hybrid optimization approach based on the Taguchi method and a best-first search algorithm is used to accelerate the search for a near optimal solution. Furthermore, sensitivity analysis and experimental results with texture mapping demonstrate the effectiveness of the proposed approach

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:31 ,  Issue: 5 )