Facial Age Estimation Using Compact Facial Features

Facial age estimation studies have shown that the type of features used for face representation significantly impact the accuracy of age estimates. This work proposes a novel method of representing the face with compact facial features derived from extracted raw image pixels and Local Binary Patterns (LBP) for age estimation. The compact facial features are realized by exploiting the statistical properties of extracted facial features while aggregating over a whole feature set. The resulting compact feature set is of reduced dimensionality compared to the non-compact features. It also proves to retain relevant facial features as it achieves better age prediction accuracy than the non-compact features. An age group ranking model is also proposed which further reduces facial features dimensionality while improving age estimation accuracy. Experiments on the publicly-available FG-NET and Lifespan datasets gave a Mean Absolute Error (MAE) of 1.76 years and 3.29 years respectively, which are the lowest MAE so far reported on both datasets, to the best of our knowledge.