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Learning a generic 3D face model from 2D image databases using incremental Structure-from-Motion
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DOI：10.1016/j.imavis.2010.01.005.png)
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Over the last decade 3D face models have been extensively used in many applications such as face recognition, facial animation and facial expression analysis. 3D Morphable Models (MMs) have become a popular tool to build and fit 3D face models to images. Critical to the success of MMs is the ability to build a generic 3D face model. Major limitations in the MMs building process are: (1) collecting 3D data usually involves the use of expensive laser scans and complex capture setups, (2) the number of available 3D databases is limited, and typically there is a lack of expression variability and (3) finding correspondences and registering the 3D model is a labor intensive and error prone process. This paper proposes an incremental Structure-from-Motion (SfM) approach to learn a generic 3D face model from large collections of existing 2D hand-labeled images containing many subjects under different expressions and poses. The two major contributions of the paper are: (1) learning a generic 3D deformable face model from 2D databases and (2) incorporating a prior subspace into the incremental SfM formulation to provide robustness to noise, missing data and degenerate shape configurations. Experimental results on the CMU-PIE database show improvements in the generalization of the 3D face model across expression and identity. (C) 2010 Elsevier B.V. All rights reserved.
Keyword:
Structure-from-Motion
Incremental learning
Morphable Models
Active Appearance Models
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