Age estimation from face images: challenging problem for audience measurement systems

The real-time audience measurement system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and in-cloud data statistics analysis. The challenging part of such system is age estimation algorithm on the basis of machine learning methods. The face aging process is determined by different factors: genetic, lifestyle, expression and environment. That is why same age people can have quite different rates of facial aging. We propose a novel algorithm consisting of two stages: adaptive feature extraction based on local binary patterns and support vector machine classification. Experimental results on the FG-NET, MORPH and our own database are presented. Human perception ability in age estimation is studied using crowdsourcing which allows a comparison of the ability of machines and humans.