A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Solid shape
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Shock Graphs and Shape Matching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based 3D Face Capture with Shape-from-Silhouettes
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Implicit Surfaces Make for Better Silhouettes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Face Processing: Advanced Modeling and Methods
Face Processing: Advanced Modeling and Methods
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In this paper we take a fresh look at the problem of extracting shape from contours of human faces. We focus on two key questions: how can we robustly fit a 3D face model to a given input contour; and, how much information about shape does a single contour image convey. Our system matches silhouettes and inner contours of a PCA based Morphable Model to an input contour image. We discuss different types of contours in terms of their effect on the continuity and differentiability of related error functions and justify our choices of error function (modified Euclidean Distance Transform) and optimization algorithm (Downhill Simplex). In a synthetic test setting we explore the limits of accuracy when recovering shape and pose from a single correct input contour and find that pose is much better captured by contours than is shape. In a semi-synthetic test setting - the input images are edges extracted from photorealistic renderings of the PCA model - we investigate the robustness of our method and argue that not all discrepancies between edges and contours can be solved by the fitting process alone.