Facial Image-Based Age Estimation

Abstract

Automatic age estimation consists of using a computer to predict the age of a person based on a given facial image. The age prediction is built on distinct patterns emerging from the facial appearance. The interest of such process has increasingly grown due to the wide range of its potential applications in law enforcement, security control, and human-computer interaction. However, the estimation problem remains challenging since it is influenced by a lot of factors including lifestyle, gender, environment, and genetics. Many recent algorithms used for automatic age estimation are based on machine learning methods and have proven their efficiency and accuracy in this domain. In this chapter, we present an empirical study on a complete age estimation system built around label sensitive learning [1]. Experimental results conducted on FG-NET and MORPH Album II face databases are presented.

Keywords: Age classification, Age estimation, Age prediction, Dimensionality reduction, Facial feature extraction, Gabor filter, K-nearest neighbors, Labelsensitive, Local binary pattern, Local regression, Locality preserving projections, Machine learning, Marginal fisher analysis, Mean absolute error, Partial least square regression, Preprocessing, Recognition rate, Support vector regression.