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

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-desi...

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Bibliographic Details
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
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Summary: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.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/3477.956032