Age estimation from a face image in a selected gender-race group based on ranked local binary patterns

An algorithm to classify people by age from face images based on a two-stage support vector regression is proposed. Only the most significant local binary patterns are used as descriptive features of an image. The distinctive feature of the proposed approach is in the use of a sequential procedure that involves classifying images of people first by gender, then by race in each gender group and only then by age within a selected gender-race group. In order to increase the accuracy of the classification, a bootstrapping procedure (learning on “hard” examples) is used at each stage. The use of this approach has made it possible to improve an accuracy of the classification by 12% for gender, by 15% for race and by 2 years for age (the Mean Absolute Error metric) in comparison with other known algorithms.