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
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
MORPH: A Longitudinal Image Database of Normal Adult Age-Progression
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Age simulation for face recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Learning from facial aging patterns for automatic age estimation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Image-Based Surface Detail Transfer
IEEE Computer Graphics and Applications
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Bayesian age estimation on face images
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
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Simulating facial aging effects is a challenge task because of the difficulties in understanding and modeling the aging pattern. In this paper, a novel aging model called Aging Increment Distribution Function was proposed to model the age progression in the statistical appearance model space. The trajectory of face samples is learned to build the distribution function with free shape. So it has finer resolution to reveal the underlying aging pattern. Based on modeling the increment of appearance parameter, an analytical framework was formulated to re-render the given face image onto any other age within the maximum age span of training samples. In experiment, the MORPH face database was used to train the aging model, which has been further applied to re-rendering of age effects. Both aging and rejuvenating simulation results presented similar effects comparing to the real images, which verified the effectiveness of proposed method.