GiMeFive: Towards Interpretable Facial Emotion Classification
Jiawen Wang Leah Kawka
Deep convolutional neural networks have been shown to successfully recognize
facial emotions for the past years in the realm of computer vision. However,
the existing detection approaches are not always reliable or explainable, we
here propose our model GiMeFive with interpretations, i.e., via layer
activations and gradient-weighted class activation mapping. We compare against
the state-of-the-art methods to classify the six facial emotions. Empirical
results show that our model outperforms the previous methods in terms of
accuracy on two Facial Emotion Recognition (FER) benchmarks and our aggregated
FER GiMeFive. Furthermore, we explain our work in real-world image and video
examples, as well as real-time live camera streams. Our code and supplementary
material are available at https: //github.com/werywjw/SEP-CVDL.