Active learning with the furthest nearest neighbor criterion for facial age estimation

Abstract

Providing training data for facial age estimation is very expensive in terms of age progress, privacy, human time and effort. In this paper, we present a novel active learning approach based on an on-line Two-Dimension Linear Discriminant Analysis for learning to quickly reach high performance but with minimal labeling effort. The proposed approach uses the classifier learnt from the small pool of labeled faces to select the most informative samples from the unlabeled set to increasingly improve the classifier. Specifically, we propose a novel data selection of the Furthest Nearest Neighbour (FNN) that generalizes the margin-based uncertainty to the multi-class case and which is easy to compute so that the proposed active learning can handle a large number of classes and large data sizes efficiently. Empirical experiments on FG-NET, Morph databases and a large unlabeled data set show that the proposed approach can achieve similar results using fewer samples than random selection. © 2011 Springer-Verlag Berlin Heidelberg.