Cross-heterogeneous-database age estimation with co-representation among them

Abstract

Human age estimation is an important research topic and can find its applications in such as commodity recommendation and security monitoring. The establishment of existing estimators basically follows a same pipeline, i.e., an estimator is built from a given training dataset like FG-NET and then evaluated on a holdout testing set to determine its effectiveness. In doing so, a usually-followed assumption is that both training and testing sets should share the same age distribution and the same feature representation, implying that 1) once the true age of a human image to be tested is out of the age range of the training set, a mis-estimation is naturally inevitable; 2) estimators built on datasets with different feature representations cannot be directly applied to make predictions on testing sets of each other unless re-trained, because their features are usually different (i.e., the databases are heterogeneous). To the best of our knowledge, the age distributions of different aging databases are usually not consistent and complementary to each other. Motivated by this fact, in order to incorporate such a complementarity in age distributions to improve the generalization ability of the age estimator, in this paper we propose a unified cross-heterogeneous-database age estimation method by first projecting the training samples, usually represented with different features, of different aging databases into a common feature space, and then constructing an age estimator in their mixed sample space. By this way, the age-distribution-incompleteness of the aging datasets can be alleviated by co-representation among them and thus the discriminating ability of the age estimator can be reinforced. Finally, experimental results demonstrate the superiority of the proposed method.