Robust Facial Feature Localization Using Data-Driven Semi-Supervised Learning Approach

In this paper, we present a novel localization method of facial feature points with generalization ability based on a data-driven semi-supervised learning approach. Even though a powerful facial feature detector can be built using a number of human-annotated training data, the collection process is time-consuming and very often impractical due to the high cost and error-prone process of manual annotations. The proposed method takes advantage of a data-driven semi-supervised learning that optimizes a hybrid detector by interacting with a hierarchical data model to suppress and regularize noisy outliers. The competitive performance comparing to other state-of-the-art technology is also shown using benchmark datasets, Bosprous, BioID.