Abstract-Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in the computer vision community. However, most algorithms are designed for faces in small to medium poses (yaw angle is smaller than 45 • ), which lack the ability to align faces in large poses up to 90 • . The challenges are three-fold. Firstly, the commonly used landmark face model assumes that all the landmarks are visible and is therefore not suitable for large poses. Secondly, the face appearance varies more drastically across large poses, from the frontal view to the profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose to tackle these three challenges in an new alignment framework termed 3D Dense Face Alignment (3DDFA), in which a dense 3D Morphable Model (3DMM) is fitted to the image via Cascaded Convolutional Neural Networks. We also utilize 3D information to synthesize face images in profile views to provide abundant samples for training. Experiments on the challenging AFLW database show that the proposed approach achieves significant improvements over the state-of-the-art methods.
A total 3D solution to face alignment even for large poses (greater than 45°and up to 90°) is proposed by Zhu et al. REF fitting 3D Morphable Models to 2D input face image by means of a cascaded CNN.
Face Alignment in Full Pose Range: A 3D Total Solution
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ArXiv
Computer Science Medicine