Direct Shape Regression Networks for End-to-End Face Alignment
端到端人脸对齐的直接形状回归网络1 主要的挑战在于人脸图像和相关的面部形状之间的高度非线性关系，这种非线性关系是基于标记的相关性耦合。现有的方法主要依赖于级联回归，存在固有的缺点，例如对初始化的强依赖性和未能利用相关的标记。 本文提出了一种**直接形状回归网络（direct shape regression network, DSRN）**用于端到端人脸对齐，它是在统一的框架下联合处理上述挑战的。
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相关文章
- 1. 人脸对齐--Face Alignment by Explicit Shape Regression
- 2. Deep Cascaded Regression for Face Alignment（DCR2015）
- 3. 《Face Alignment by Explicit Shape Regression》笔记
- 4. POCR-project-out cascaded regression for face alignment
- 5. (Face Alignment) One Millisecond Face Alignment with an Ensemble of Regression Trees 笔记
- 6. 学习笔记：Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment
- 7. Face Alignment
- 8. Occlusion-free Face Alignment: Deep Regression Networks Coupled with De-corrupt AutoEncoders
- 9. Face Alignment Survey
- 10. Deep Alignment Network: A convolutional neural network for robust face alignment