Numerical stability of algorithms for 2D Delaunay triangulations
SCG '92 Proceedings of the eighth annual symposium on Computational geometry
Multiresolution sampling procedure for analysis and synthesis of texture images
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Expressive expression mapping with ratio images
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Real-time texture synthesis by patch-based sampling
ACM Transactions on Graphics (TOG)
Manipulating Facial Appearance through Shape and Color
IEEE Computer Graphics and Applications
Prototyping and Transforming Facial Textures for Perception Research
IEEE Computer Graphics and Applications
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skin Aging Estimation by Facial Simulation
CA '99 Proceedings of the Computer Animation
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ACM SIGGRAPH 2003 Papers
Modeling Age Progression in Young Faces
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Modeling Age Progression in Young Faces
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Generic Framework for Efficient 2-D and 3-D Facial Expression Analogy
IEEE Transactions on Multimedia
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Face aging aims at synthesizing one's face at different ages which interests many researchers in fields of cartoon animation, age estimation, face recognition, etc. However, modelling aging process is still challenging due to lack of robust features and a reasonable training set. In this paper we propose a novel automatic face aging approach through Maximum A Posteriori (MAP), a face is firstly warped by global geometric transformation, and then elder skin is locally synthesized by sparse coding. Our contribution includes an aging model both for children growth and adults aging, and high-level features by sparse representation aiming at a small training set while not downgrading the quality of synthesis. Moreover, the newly proposed features simplify the algorithm and lead to a fast implementation. Experiments show the proposed approach outperforms the existing methods.