Face Alignment with Two-Layer Shape Regression

We present a novel approach to resolve the problem of face alignment with a two-layer shape regression framework. Traditional regression-based methods [4, 6, 7] regress all landmarks in a single shape without consideration of the difference between various landmarks in biologic property and texture, which would lead to a suboptimal prediction. Unlike previous regression-based approach, we do not regress the entire landmarks in a holistic manner without any discrimination. We categorize the geometric constraints into two types, inter-component constraints and intra-component constraints. Corresponding to these two shape constraints, we design a two-layer shape regression framework which can be integrated with regression-based methods. We define a term of “key points” of components to describe inter-component constraints and then determine the sub-shapes. We verify our two-layer shape regression framework on two widely used datasets (LFPW [10] and Helen [11]) for face alignment and experimental results prove its improvements in accuracy.