Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model

TLDR

This paper addresses facial landmark localization and tracking from a single camera. The study proposes a two‑stage cascaded deformable shape model to efficiently localize facial landmarks under large head‑pose variations. The method employs a group‑sparse landmark selector, 3D shape‑based pose‑free initialization via Procrustes analysis, a mean‑shift local search with constrained local models, and component‑wise active contours for refinement, evaluated on laboratory and in‑the‑wild datasets. The framework achieves real‑time face detection, pose‑free landmark localization, and tracking, outperforming state‑of‑the‑art methods in handling pose variations.

Abstract

This paper addresses the problem of facial landmark localization and tracking from a single camera. We present a two-stage cascaded deformable shape model to effectively and efficiently localize facial landmarks with large head pose variations. For face detection, we propose a group sparse learning method to automatically select the most salient facial landmarks. By introducing 3D face shape model, we use procrustes analysis to achieve pose-free facial landmark initialization. For deformation, the first step uses mean-shift local search with constrained local model to rapidly approach the global optimum. The second step uses component-wise active contours to discriminatively refine the subtle shape variation. Our framework can simultaneously handle face detection, pose-free landmark localization and tracking in real time. Extensive experiments are conducted on both laboratory environmental face databases and face-in-the-wild databases. All results demonstrate that our approach has certain advantages over state-of-the-art methods in handling pose variations.