Shape Initialization Without Ground Truth For Face Alignment

The shape initialization is a crucial step for face alignment. In the literature, many approaches use the ground truth points to compute the bounding box. However, it is not always possible to detect an accurate bounding box in real applications due to various adverse factors. In this work, an effective initialization approach for face alignment is proposed. Firstly a modified Deformable Part Models (DPM) is used to estimate the face pose and the bounding box to obtain an initial shape. Then by detecting the two pupils, the roll rotation of the face is measured to correct the initial shape. To further increase the robustness and accuracy of face alignment, multiple initial shapes for each face are generated, then each one is refined by a cascade regression-based approach and we can get multiple shape estimations. Finally a better final shape is obtained by fusing the multiple estimations via the structured SVM learning. Experiments on challenging datasets and comparison with the state-of-the-art methods validate our proposed method in unconstrained environment.