Paper：Extended deep neural network for facial emotion recognition（2019)
目录 总结： 1. 模型和结果 1.1 模型参数 1.2 模型方法 2. 数据处理 2.1 裁剪 2.2 ntensity normalization 思考和收获 总结： 用一个简单的CNN模型实现了CK+和JAFFE数据集的表情识别。 该模型仅仅使用了卷积层和 深度残差模块，达到了 93.24% 的准确度。 1. 模型和结果 1.1 模型参数 优化方法：SGD batch size ：64 学习
相关文章
- 1. Recurrent Neural Networks for Emotion Recognition in Video
- 2. Deep Covariance Descriptors for Facial Expression Recognition
- 3. 2015 ICCV论文《Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition》
- 4. ICCV2015论文 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. 表情识别入门：ECCV2016《Peak-Piloted Deep Network for Facial Expression Recognition》
- 7. ECCV2016论文 Peak-piloted deep network for facial expression Recognition 解析
- 8. 2016 ECCV论文 《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
- 更多相关文章...
- • Swift for 循环 - Swift 教程
- • Scala for循环 - Scala教程
- • Flink 数据传输及反压详解
- • Java Agent入门实战（三）-JVM Attach原理与使用
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. Recurrent Neural Networks for Emotion Recognition in Video
- 2. Deep Covariance Descriptors for Facial Expression Recognition
- 3. 2015 ICCV论文《Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition》
- 4. ICCV2015论文 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. 表情识别入门：ECCV2016《Peak-Piloted Deep Network for Facial Expression Recognition》
- 7. ECCV2016论文 Peak-piloted deep network for facial expression Recognition 解析
- 8. 2016 ECCV论文 《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