Age estimation using active appearance models and ensemble of classifiers with dissimilarity-based classification

Abstract

This paper proposes a novel technique that uses Active Appearance Models (AAMs) and Ensemble of classifiers for age estimation. In this technique, features are extracted from face images by AAMs and a global classifier is then used to obtain an idea about the age by distinguishing between child/teen-hood and adulthood, before age estimation. This is done by an ensemble containing various classifiers trained on multiple dissimilarities and thereby which reduces misclassification error. Different aging functions are considered for the classified images to estimate age more accurately. Experiments are performed on the publicly available FG-NET database. The method is found to be a good age estimator. © 2011 Springer-Verlag.