| Fast Multi-view Face Alignment via Multi-task Auto-encoders | |
Qi Li ; Zhenan Sun; Ran He(赫然)
| |
| 2017-10 | |
| 会议名称 | International Joint Conference on Biometrics (IJCB) |
| 会议日期 | 2017-10 |
| 会议地点 | Denver, America |
| 摘要 | Face 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. |
| 收录类别 | EI |
| 文献类型 | 会议论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/19691 |
| 专题 | 智能感知与计算研究中心 |
| 作者单位 | Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China |
| 第一作者单位 | 模式识别国家重点实验室 |
| 推荐引用方式 GB/T 7714 | Qi Li,Zhenan Sun,Ran He. Fast Multi-view Face Alignment via Multi-task Auto-encoders[C],2017. |
| 条目包含的文件 | 下载所有文件 | |||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| 0172.pdf（302KB） | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 | ||
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