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.
