Automated 3D Face Reconstruction from Multiple Images Using Quality Measures

Automated 3D reconstruction of faces from images is challenging if the image material is difficult in terms of pose, lighting, occlusions and facial expressions, and if the initial 2D feature positions are inaccurate or unreliable. We propose a method that reconstructs individual 3D shapes from multiple single images of one person, judges their quality and then combines the best of all results. This is done separately for different regions of the face. The core element of this algorithm and the focus of our paper is a quality measure that judges a reconstruction without information about the true shape. We evaluate different quality measures, develop a method for combining results, and present a complete processing pipeline for automated reconstruction.