Age classification from facial images
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Automatic Simulation of Aging Effects on Face Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
A tutorial on support vector regression
Statistics and Computing
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
Learning from facial aging patterns for automatic age estimation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Facial Expression Recognition using AAM and Local Facial Features
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Age estimation using active appearance models and support vector machine regression
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Human Age Estimation With Regression on Discriminative Aging Manifold
IEEE Transactions on Multimedia
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This paper proposes a novel technique that uses Active Appearance Models (AAMs) and Ensemble of classifiers for age estimation. In this technique, features are extracted from face images by AAMs and a global classifier is then used to obtain an idea about the age by distinguishing between child/teen-hood and adulthood, before age estimation. This is done by an ensemble containing various classifiers trained on multiple dissimilarities and thereby which reduces misclassification error. Different aging functions are considered for the classified images to estimate age more accurately. Experiments are performed on the publicly available FG-NET database. The method is found to be a good age estimator.