Abstract
We present a new bootstrapping algorithm to automatically enhance a 3D morphable face model with new face data. Our algorithm is based on a morphable model fitting method that uses a set of predefined face components. This fitting method produces accurate model fits to 3D face data with noise and holes. In the fitting process, the dense point-to-point correspondences between the scan data and the face model may become less reliable at the borders of components. We solve this by introducing a blending technique that improves on the distorted correspondences close to the borders. Afterwards, a new face instance is acquired similar to the 3D scan data and in full correspondence with the face model. These newly generated face instances can then be added to the morphable face model to build a more descriptive one. To avoid our bootstrapping algorithm from needlessly adding redundant face data, we incorporate a redundancy estimation algorithm. We tested our bootstrapping algorithm on a set of scans acquired with different scanning devices, and on the UND data set. Quantitative and qualitative evaluation shows that our algorithm successfully enhances an initial morphable face model with new face data, in a fully automatic manner.