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
Trainable videorealistic speech animation
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Face Identification by Fitting a 3D Morphable Model Using Linear Shape and Texture Error Functions
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A bootstrapping algorithm for learning linear models of object classes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Gaze Estimation Using Morphable Models
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Vectorizing Face Images by Interleaving Shape and Texture Computations
Vectorizing Face Images by Interleaving Shape and Texture Computations
Multidimensional morphable models: a framework for representing and matching object classes
Multidimensional morphable models: a framework for representing and matching object classes
Multidimensional Morphable Models
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A constrained hybrid optimization algorithm for morphable appearance models
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
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Multidimensional Morphable Model is a powerful model to analyze and synthesize human faces. However, the stochastic gradient descent algorithm adopted to match the Morphable Model to a novel face image is not efficient enough. In this paper, a very efficient optimization method devised for Morphable Model matching is proposed, called Active Morphable Model (AMM). The kernel of AMM is an iterative algorithm directly utilizing the heuristic information provided by the novel image, and updating the model parameters in a computationally economic fashion. AMM is more efficient than general optimization methods in matching a Morphable Model, it has much higher convergent rate and matching speed. Furthermore, it is insensitive to the initial estimation of the face pose, and is robust when used to match novel faces with large variations in translation, rotation and scaling. Experimental results are given to validate the efficiency and robustness of the proposed method.