【论文阅读】Recognizing Facial Expressions of Occluded Faces using Convolutional Neural Networks

1.这篇文章究竟讲了什么问题？
使用卷积神经网络识别遮挡下的人脸表情
2.这是否是一个新的问题？
不是
3.这篇文章要验证一个什么科学假设？
卷积神经网络模型在半遮挡数据集上训练，能够比在全脸数据集上训练的精度要好
4.有哪些相关研究？如何归类？谁是这一课题在这领域值得关注的研究员？
人脸表情识别论文：

1. Barsoum, E., Zhang, C., Ferrer, C.C., Zhang, Z.: Training deep networks for facial expression recognition with crowd-sourced label distribution. In: Proceedings of ICMI. pp. 279–283 (2016)
2. Ding, H., Zhou, S.K., Chellappa, R.: Facenet2expnet: Regularizing a deep face recognition net for expression recognition. In: Proceedings of FG. pp. 118–126(2017)
3. Georgescu, M.I., Ionescu, R.T., Popescu, M.: Local Learning with Deep and Hand-crafted Features for Facial Expression Recognition. IEEE Access 7, 64827–64836(2019)
4. Giannopoulos, P., Perikos, I., Hatzilygeroudis, I.: Deep learning approaches for facial emotion recognition: A case study on fer-2013. In: Advances in Hybridization of Intelligent Methods, pp. 1–16. Springer (2018)
5. Hasani, B., Mahoor, M.H.: Facial expression recognition using enhanced deep 3d convolutional neural networks. In: Proceedings of CVPR W. pp. 2278–2288 (2017)
6. Hua, W., Dai, F., Huang, L., Xiong, J., Gui, G.: HERO: Human Emotions Recognition for Realizing Intelligent Internet of Things. IEEE Access 7, 24321–24332(2019)
7. Kim, B.K., Roh, J., Dong, S.Y., Lee, S.Y.: Hierarchical committee of deep convolutional neural networks for robust facial expression recognition. Journal on Multimodal User Interfaces 10(2), 173–189 (2016)
8. Li, D., Wen, G.: MRMR-based ensemble pruning for facial expression recognition. Multimedia Tools and Applications pp. 1–22 (2017)
9. Li, S., Deng, W., Du, J.: Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: Proceedings of CVPR. pp. 2584–2593(2017)
10. Li, Y., Zeng, J., Shan, S., Chen, X.: Patch-Gated CNN for occlusion-aware facial expression recognition. In: Proceedings of ICPR. pp. 2209–2214 (2018)
11. Liu, X., Kumar, B., You, J., Jia, P.: Adaptive deep metric learning for identity-aware facial expression recognition. In: Proceedings of CVPR W. pp. 522–531 (2017)
12. Meng, Z., Liu, P., Cai, J., Han, S., Tong, Y.: Identity-aware convolutional neural network for facial expression recognition. In: Proceedings of FG. pp. 558–565 (2017)
13. Mollahosseini, A., Hassani, B., Salvador, M.J., Abdollahi, H., Chan, D., Mahoor, M.H.: Facial expression recognition from World Wild Web. In: Proceedings of CVPR W. pp. 1509–1516 (2016)
14. Wen, G., Hou, Z., Li, H., Li, D., Jiang, L., Xun, E.: Ensemble of deep neural networks with probability-based fusion for facial expression recognition. Cognitive Computation 9(5), 597–610 (2017)
15. Yu, Z., Zhang, C.: Image based static facial expression recognition with multiple deep network learning. In: Proceedings of ICMI. pp. 435–442. ACM (2015)
16. Zeng, J., Shan, S., Chen, X.: Facial expression recognition with inconsistently annotated datasets. In: Proceedings of ECCV. pp. 222–237 (2018)
    遮挡人脸表情识别：Li, Y., Zeng, J., Shan, S., Chen, X.: Patch-Gated CNN for occlusion-aware facial expression recognition. In: Proceedings of ICPR. pp. 2209–2214
    VR下人脸表情识别：
    Hickson, S., Dufour, N., Sud, A., Kwatra, V., Essa, I.: Eyemotion: Classifying facial expressions in VR using eye-tracking cameras. In: Proceedings of W ACV. pp. 1626–1635 (2019)

5.论文中提到的解决方案之关键是什么？
在半遮挡数据集上训练了VGG-F和VGG-Face两个模型

6.论文中的实验是如何设计的？
在AffectNet和FER+混合数据集上进行训练，训练数据集和测试数据集都包括全脸和半脸两种，与Bag-of-visaul-word，VGG-13以及AlexNet进行了精度对比

7.用于定量评估的数据集是什么？代码有没有开源？
AffectNet和FER+，没有找到开源

8.论文中的实验及结果有没有很好地支持需要验证的科学假设？
有

9.这篇论文到底有什么贡献？
提出在半遮挡下的表情识别方法，通过在半遮挡数据集上训练以及测试，达到较好的识别效果。

10.下一步呢？有什么工作可以继续深入？
可以借鉴到半遮挡下的微表情识别。