Neural Computation
Age classification from facial images
Computer Vision and Image Understanding
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
Modeling Age Progression in Young Faces
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Learning from facial aging patterns for automatic age estimation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Automatic Age Estimation Based on Facial Aging Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial age estimation by nonlinear aging pattern subspace
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Human Age Estimation With Regression on Discriminative Aging Manifold
IEEE Transactions on Multimedia
Comparing different classifiers for automatic age estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Face Verification Across Age Progression
IEEE Transactions on Image Processing
Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression
IEEE Transactions on Image Processing
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Age problems have attracted many researchers' attentions in recent years since they have many potential applications in human-computer interaction and other areas. Among all the age problems, automatic age estimation is one interesting problem and many methods have been proposed to solve this problem. In this paper, we use two Bayesian process regression algorithms, Gaussian process and t process, for age estimation. Different from previous regression methods on age estimation, which need to specify the form of regression functions or determine many parameters in regression functions in inefficient ways such as cross validation, in our methods, the form of regression function is implicitly defined by kernel function and almost all the parameters of our methods can be learnt from data automatically using efficient gradient methods. Moreover, our methods are very simple and easy to implement. Since Gaussian process is easy to be affected by outlier data points, t process can be viewed as a robust version of Gaussian process to solve this problem. Experiments on one public aging database FG-NET show our method is effective and comparable with the state-of-the-art methods on age estimation.