Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prototyping and Transforming Facial Textures for Perception Research
IEEE Computer Graphics and Applications
Toward Automatic Simulation of Aging Effects on Face Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Active Appearance Models
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Towards Automatic Face Identification Robust to Ageing Variation
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Verification across Age Progression
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
MORPH: A Longitudinal Image Database of Normal Adult Age-Progression
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Computational methods for modeling facial aging: A survey
Journal of Visual Languages and Computing
A comparative study of active appearance model annotation schemes for the face
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
An improved rendering technique for active-appearance-model-based automated age progression
ACM SIGGRAPH 2013 Posters
Hi-index | 0.00 |
Normal adult aging in the face can drastically affect performance of face recognition systems. Synthetically generating age-progressed or age-regressed images for aiding recognizers is one method of improving the robustness of face-based biometrics. These synthetic age progressions may also aid human law enforcement and other applications. There has been wide interest in these techniques in recent years, and the use of Active Appearance Models (AAMs) for synthetic age progression has been shown to be a promising approach but has not yet been demonstrated on a large human population with wide variation. This paper presents improvements in AAM-based age progression that generate significantly improved visual results, taking into account a much wider gender, age, and ethnic range than published to date for age progression techniques.