Metric learning for regression problems and human age estimation

The estimation of human age from face images has great potential in real-world applications. However, how to discover the intrinsic aging trend is still a challenging problem. In this work, 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. Through 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 available FG-NET database by gaussian process regression in the learned metric to validate our methods and the age estimation performance is improved over the traditional regression methods.