A hierarchical approach for human age estimation

We consider the problem of automatic age estimation from face images. Age estimation is usually formulated as a regression problem relating the facial features and the age variable, and a single regression model is learnt for all ages. We propose a hierarchical approach, where we first divide the face images into various age groups and then learn a separate regression model for each group. Given a test image, we first classify the image into one of the age groups and then use the regression model for that particular group. To improve our classification result, we use many different classifiers and fuse them using the majority rule. Experiments show that our approach outperforms many state of the art regression methods for age estimation.