Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
527266 | Image and Vision Computing | 2010 | 13 Pages |
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.