Metric Learning for Regression Problems and Human Age Estimation

The estimation of human age how face images has great. potential in real-world applications However, how to discover the intrinsic aging trend is still a challenging problem this work, we proposed a. general distance metric learning scheme for regression problems which utilizes not only data themselves. but also then. corresponding labels to strengthen the credibility of distances This metric could be learned by solving,,uti optimization problem Through the learned metric, it is easy to find the intrinsic variation trend of data by a whiny small amount, of samples without, any prior knowledge of the structure or disturbtion of data Furthermore, the test data could be projected to this metric by a simple lineal transformation and it is easy to be combined with manifold learning algorithms to improve the performance Experiments ate conducted on the public available VG-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