Toward Automatic Simulation of Aging Effects on Face Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Age Progression in Young Faces
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Automatic Age Estimation Based on Facial Aging Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new algorithm for age recognition from facial images
Signal Processing
A Ranking Approach for Human Ages Estimation Based on Face Images
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Age Synthesis and Estimation via Faces: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Person-specific age estimation under ranking framework
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Comparing different classifiers for automatic age estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Face Image Modeling by Multilinear Subspace Analysis With Missing Values
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Different from the traditional age estimation systems, this paper introduces a novel method to estimate human age from facial images combined with non-fixed age group classification and Rank-based age value estimation. The basic idea is to use the continuous and ordinal information from human facial aging process. Firstly, pair-wise distance classifiers are employed to roughly estimate age within total database. Then a flexible and more precise age group is decided by extending the rough age value to a specific age scope. For the ranking framework of precious age estimation, by calculating the continental distances to select the most similar sample from the training database. The average label of the voted samples is used to predict age. In our proposed system, six-dimension shape features and different texture features are used to describe facial images. Tested on FG-NET database, our system achieves 4.89 evaluated by MAE (Mean Absolute Error).