CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术研究
Face Alignment Across Large Poses: A 3D Solution
Zhu XY(朱翔昱)1; Lei Z(雷震)1; Liu XM(刘晓明)2; Shi HL(石海林)1; Li ZQ(李子青)1
Conference NameIn IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Conference DateJune 26 - July 1, 2016
Conference PlaceLas Vegas, NV, USA
AbstractFace alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community. However, most algorithms are designed for faces in small to medium poses (below 45 degrees), lacking the ability to align faces in large poses up to 90 degrees. The challenges are three-fold: Firstly, the commonly used landmark-based face model assumes that all the landmarks are visible and is therefore not suitable for profile views. Secondly, the face appearance varies more dramatically across large poses, ranging from frontal view to profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN). We also propose a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling. Experiments on the challenging AFLW database show that our approach achieves significant improvements over state-of-the-art methods.
Indexed ByEI
Document Type会议论文
2.Department of Computer Science and Engineering, Michigan State University
Recommended Citation
GB/T 7714
Zhu XY,Lei Z,Liu XM,et al. Face Alignment Across Large Poses: A 3D Solution[C],2016.
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