Trainable method of parametric shape description
Image and Vision Computing - Special issue: BMVC 1991
Active shape models—their training and application
Computer Vision and Image Understanding
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Reconstruction and representation of 3D objects with radial basis functions
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
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
Journal of Cognitive Neuroscience
Identities, forgeries and disguises
International Journal of Information Technology and Management
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In this paper, we present an efficient algorithm for reconstructing 3D head model from a single 2D image based on using a 3D eigenhead model. This system is composed of two components, offline training of the eigenhead model and online reconstruction of a 3D head model. For the first part, we propose a new 3D head alignment algorithm based on an iterative coarse-to-fine scheme to establish dense point correspondences between 3D head model in the cylindrical coordinate to align the 3D head models in the training data set. In addition, we apply the radial basis function technique to establish dense correspondences between each 3D face model and a reference face model, followed by the principal component analysis technique to compute the statistical eigenhead model. For the 3D face reconstruction from a single image, the proposed algorithm finds the best linear combination of the eigenhead bases that minimizes an energy function composed of distances between the corresponding facial feature points and a one-way partial Haussdorf distance between the facial contours in the image domain. This energy minimization is accomplished by the iterative Levenberg-Marquardt algorithm with the initial guess determined by solving a linear system derived from the image projection constraints for the corresponding facial feature points. Experimental results show that the proposed 3D face reconstruction algorithm provides satisfactory results and takes less than 10 seconds on a regular PC.