Exploiting Unlabeled Ages for Aging Pattern Analysis on a Large Database

Abstract

Big Data analysis is an emerging topic in computer vision and pattern recognition. As one example problem of big data, we study semantic age labels and facial aging pattern analysis on a large database. In aging analysis, one of the great challenges is the lack of a large number of face images with ground truth age labels. Unlike many other example-based recognition problems where human annotations can be used as the ground truth labels for both training and testing, it is quite difficult to label the exact ages in face images by human annotators. An alternative is to exploit the unlabeled ages to enhance the age estimation performance. However, it is unclear whether the face images with unlabeled ages can be used or not for age estimation, and how to use the unlabeled data. In this paper, we study the two problems comprehensively under two paradigms: the semi-supervised learning and unsupervised learning for aging pattern analysis. We emphasize the importance of using ground truth age labels and a large database in order to derive a meaningful measure in the context of big data. Our study can make an impact on collecting aging patterns that is very expensive and time consuming in practice.