Paper：Extended deep neural network for facial emotion recognition（2019)
目錄 總結： 1. 模型和結果 1.1 模型參數 1.2 模型方法 2. 數據處理 2.1 裁剪 2.2 ntensity normalization 思考和收穫 總結： 用一個簡單的CNN模型實現了CK+和JAFFE數據集的表情識別。 該模型僅僅使用了卷積層和 深度殘差模塊，達到了 93.24% 的準確度。 1. 模型和結果 1.1 模型參數 優化方法：SGD batch size ：64 學習
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相關文章
- 1. Recurrent Neural Networks for Emotion Recognition in Video
- 2. Deep Covariance Descriptors for Facial Expression Recognition
- 3. 2015 ICCV論文《Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition》
- 4. ICCV2015論文 Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition解析
- 5. [NLU] DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
- 6. 表情識別入門：ECCV2016《Peak-Piloted Deep Network for Facial Expression Recognition》
- 7. ECCV2016論文 Peak-piloted deep network for facial expression Recognition 解析
- 8. 2016 ECCV論文 《Peak-Piloted Deep Network for Facial Expression Recognition》
- 9. Deep Facial Expression Recognition: A Survey
- 10. CVPR：Weakly-supervised Deep Convolutional Neural Network Learning for Facial Action Intensity Estima