Facial Expression Recognition by De-expression Residue Learning閱讀筆記
預覽 這是一篇CVPR2018關於表情識別的論文，作者可謂是獨闢蹊徑，從「De-expression」這一角度進行表情識別的研究。作者通過一些事實和文獻發現，人的表情可以分解爲Neutral Compoent和Expressive Componet兩部分。作者的想法是將人臉經過一個GAN網絡得到一張與之對應的中性表情，然後對residue(殘餘特徵)進行訓練學習，進一步進行表情分類。 原文地址 原
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相關文章
- 1. Facial Expression Recognition by De-expression Residue Learning
- 2. Facial Expression Recognition by De-expression Residue Learning-論文筆記
- 3. A Compact Deep Learning Model for Robust Facial Expression Recognition 閱讀筆記
- 4. 2018CVPR：Joint Pose and Expression Modeling for Facial Expression Recognition閱讀筆記
- 5. FERAtt: Facial Expression Recognition with Attention Net閱讀筆記
- 6. Suppressing Uncertainties for Large-Scale Facial Expression Recognition閱讀筆記
- 7. Deep Facial Expression Recognition: A Survey 筆記
- 8. Facial Expression Recognition Using KCCA
- 9. CVPR2020: Suppressing Uncertainties for Large-Scale Facial Expression Recognition 閱讀隨筆
- 10. Deep Covariance Descriptors for Facial Expression Recognition