Facial Expression Recognition by De-expression Residue Learning阅读笔记
预览 这是一篇CVPR2018关于表情识别的论文，作者可谓是独辟蹊径，从“De-expression”这一角度进行表情识别的研究。作者通过一些事实和文献发现，人的表情可以分解为Neutral Compoent和Expressive Componet两部分。作者的想法是将人脸经过一个GAN网络得到一张与之对应的中性表情，然后对residue(残余特征)进行训练学习，进一步进行表情分类。 原文地址 原
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相关文章
- 1. Facial Expression Recognition by De-expression Residue Learning
- 2. Facial Expression Recognition by De-expression Residue Learning-论文笔记
- 3. A Compact Deep Learning Model for Robust Facial Expression Recognition 阅读笔记
- 4. 2018CVPR：Joint Pose and Expression Modeling for Facial Expression Recognition阅读笔记
- 5. FERAtt: Facial Expression Recognition with Attention Net阅读笔记
- 6. Suppressing Uncertainties for Large-Scale Facial Expression Recognition阅读笔记
- 7. Deep Facial Expression Recognition: A Survey 笔记
- 8. Facial Expression Recognition Using KCCA
- 9. CVPR2020: Suppressing Uncertainties for Large-Scale Facial Expression Recognition 阅读随笔
- 10. Deep Covariance Descriptors for Facial Expression Recognition