Face Anti-Spoofing簡記1-Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture
創新點 1.使用LSTM-CNN 結構學習時間特徵和動作信息 2.歐拉運動放大法作爲面部表情的預處理 3.注意力機制用於在LSTM中選擇關鍵幀 4.合併的loss 總結 表情放大： 《 Eulerian video magnification for revealing subtle changes in the world》提出了歐拉運動信息放大方法，用於揭示視頻中不顯眼的行爲信息。上圖： 從圖中
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相關文章
- 1. Integration of image quality and motion cues for face anti-spoofing A neural network approach
- 2. Transfer Learning Using ConvolutionalNeural Networks for Face Anti-spoofifing
- 3. 【論文翻譯】Integration of image quality and motion cues for face anti-spoofing: A neural network approa
- 4. Learning Temporal Features Using LSTM-CNN Architecture for Face Anti-spoofifing
- 5. 【論文解讀】Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture|利用CNN-LSTM結構增強人臉反欺詐
- 6. 【opencv】Face Detection using Haar Cascades
- 7. Face Morph Using OpenCV — C++ / Python
- 8. Attribute-Guided Face Generation Using Conditional CycleGAN
- 9. Face Paper：Face Attention Network: An Effective Face Detector for the Occluded Faces
- 10. [reading notes]openface for face recognition