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Fast Multi-view Face Alignment via Multi-task Auto-encoders
Qi Li; Zhenan Sun; Ran He(赫然)
Conference NameInternational Joint Conference on Biometrics (IJCB)
Conference Date2017-10
Conference PlaceDenver, America
AbstractFace alignment is an important problem in computer vision. It is still an open  problem due to the variations of facial attributes (~\eg, head pose, facial expression, illumination variation). Many studies have shown that face alignment and facial attribute analysis are often correlated. This paper develops a two-stage multi-task Auto-encoders framework for fast face alignment by incorporating head pose information to handle large view variations. In the first and second stages, multi-task Auto-encoders are used to roughly locate and further refine facial landmark locations with related pose information, respectively. Besides, the shape constraint is naturally encoded into our two-stage face alignment framework to preserve facial structures. A coarse-to-fine strategy is adopted to refine the facial landmark results with the shape constraint. Furthermore, the computational cost of our method is much lower than its deep learning competitors. Experimental results on various challenging datasets show the effectiveness of the proposed method.
Indexed ByEI
Document Type会议论文
AffiliationCenter for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Qi Li,Zhenan Sun,Ran He. Fast Multi-view Face Alignment via Multi-task Auto-encoders[C],2017.
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