Human Age Estimation by Metric Learning for Regression Problems

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.