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

• Published in: IEEE transactions on cybernetics 2001-10, Vol.31 (5), p.706-719
• Main Authors: Ho, S Y, Huang, H L
• Format: Article
• Language: English
• Subjects: Acceleration; Cybernetics; Face detection; Facial; Facial features; Genetic algorithms; Humans; Image coding; Mathematical models; Optimization; Optimization methods; Searching; Sensitivity analysis; Signal processing algorithms; Solid modeling; Studies; Three dimensional; Topology
• ISSN: 1083-4419; 2168-2267; 1941-0492; 2168-2275
• DOI: 10.1109/3477.956032

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.