Simple CNN model for FER2013 dataset with 64.78 accuracy on test data. The FER2013[1], was a challenge proposed on Kaggle which was won by the team reaching the test accuracy of 75.2%. The data set consists various 48x48 images partitioned into training, validation and testing data. The images are labelled for emotions and the labels are as follows:
- Angry
- Disgust
- Fear
- Happy
- Sad
- Surprise
- Neutral
Human accuracy on the dataset is 65.5%[2] and a simple CNN model can be trained to achieve that accuracy.
- I. J. Goodfellow, D. Erhan, P. L. Carrier, A. Courville, M. Mirza, B. Hamner, W. Cukierski, Y. Tang, D. Thaler, D.-H. Lee, Y. Zhou, C. Ramaiah, F. Feng, R. Li, X. Wang, D. Athanasakis, J. Shawe-Taylor, M. Milakov, J. Park, R. Ionescu, M. Popescu, C. Grozea, J. Bergstra, J. Xie, L. Romaszko, B. Xu, Z. Chuang, and Y. Bengio. Challenges in representation learning: A report on three machine learning contests. Neural Networks, 64:59--63, 2015. Special Issue on "Deep Learning of Representations"
- Christopher Pramerdorfer, Martin Kampel, Facial Expression Recognition using Convolutional Neural Networks: State of the Art, https://arxiv.org/pdf/1612.02903.pdf