IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Toward Automatic Simulation of Aging Effects on Face Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Learning from facial aging patterns for automatic age estimation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Learning distance function by coding similarity
Proceedings of the 24th international conference on Machine learning
Regression on manifolds using kernel dimension reduction
Proceedings of the 24th international conference on Machine learning
Geodesic Gaussian kernels for value function approximation
Autonomous Robots
Facial age estimation by nonlinear aging pattern subspace
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Comparing different classifiers for automatic age estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Orthogonal Laplacianfaces for Face Recognition
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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The estimation of human age from face images has many real-world applications. However, how to discover the intrinsic aging trend is still a challenging problem. We proposed a general distance metric learning scheme for regression problems, which utilizes not only data themselves, but also their corresponding labels to strengthen the credibility of distances. This metric could be learned by solving an optimization problem. Via the learned metric, it is easy to find the intrinsic variation trend of data by a relative small amount of samples without any prior knowledge of the structure or distribution of data. Furthermore, the test data could be projected to this metric by a simple linear transformation and it is easy to be combined with manifold learning algorithms to improve the performance. Experiments are conducted on the public FG-NET database by Gaussian process regression in the learned metric to validate our framework, which shows that its performance is improved over traditional regression methods.