Human Age Estimation by Metric Learning for Regression Problems

摘要

The estimation of human age from face images is an interesting problem in computer vision. 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. Furthermore, the test data could be projected to this metric by a simple linear transformation and it is feasible to be combined with manifold learning algorithms to improve their 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 the performance is improved over traditional methods.