Unconstrained Face Alignment via Cascaded Compositional Learning
文献类型：会议论文
| 作者 | Shizhan Zhu; Cheng Li; Chen Change Loy; Xiaoou Tang |
| 出版日期 | 2016 |
| 会议名称 | CVPR2016 |
| 会议地点 | 美国 |
| 英文摘要 | We present a practical approach to address the problem of unconstrained face alignment for a single image. In our unconstrained problem, we need to deal with large shape and appearance variations under extreme head poses and rich shape deformation. To equip cascaded regressors with the capability to handle global shape variation and irregular appearance-shape relation in the unconstrained scenario, we partition the optimisation space into multiple domains of homogeneous descent, and predict a shape as a composition of estimations from multiple domain-specific regressors. With a specially formulated learning objective and a novel tree splitting function, our approach is capable of estimating a robust and meaningful composition. In addition to achieving state-of-the-art accuracy over existing approaches, our framework is also an efficient solution (350 FPS), thanks to the on-the-fly domain exclusion mechanism and the capability of leveraging the fast pixel feature |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10018]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2016 |
| 推荐引用方式 GB/T 7714 | Shizhan Zhu,Cheng Li,Chen Change Loy,et al. Unconstrained Face Alignment via Cascaded Compositional Learning[C]. 见:CVPR2016. 美国. |
入库方式： OAI收割
来源：深圳先进技术研究院
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