Age regression from faces using random forests

Predicting the age of a person through face image analysis holds the potential to drive an extensive array of real world applications from human computer interaction and security to advertising and multimedia. In this paper the first application of the random forest for age regression is proposed. This method offers the advantage of few parameters that are relatively easy to initialize. Our method learns salient anthropometric quantities without a prior model. Significant implications include a dramatic reduction in training time while maintaining high regression accuracy throughout human development.