Abstract
Frontal face synthesis plays an important role in many fields. The existing methods mainly synthesize frontal face based on the consistency assumption that non-frontal and frontal face manifolds are locally isometric. But the assumption couldn’t be held well when non-frontal faces have large variations. To solve this problem, we propose a stepwise frontal face synthesis approach for large pose non-frontal facial image. Considering that the consistency is desirable when the angle variations of different poses are small, we divide frontal face synthesis into multiple stepwise synthesis steps. In each step, the intermediate pose training sets between non-frontal and frontal training sets are used to synthesize intermediate pose faces. Furthermore, in each step, we utilize the geometric structure of target face space with small pose as constraint to represent the input face with larger pose. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods quantitatively and qualitatively.
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Acknowledgement
The research is supported by the National Nature Science Foundation of China (61231015, 61303114), National High Technology Research and Development Program of China (863 Program, No. 2015AA016306), Internet of Things Development Funding Project of Ministry of industry in 2013 (No. 25), Technology Research Program of Ministry of Public Security (2014JSYJA016), Nature Science Foundation of Hubei Province (2014CFB712).
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Wei, X., Hu, R., Han, Z., Chen, L., Ding, X. (2016). A Stepwise Frontal Face Synthesis Approach for Large Pose Non-frontal Facial Image. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_43
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DOI: https://doi.org/10.1007/978-3-319-48896-7_43
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