This repository contains code supporting publication Classifying and Visualizing Emotions with Emotional DAN (https://arxiv.org/abs/1810.10529)
We propose a new approach to solve emotion recognition task that relies on incorporating facial landmarks as a part of the classification loss function. To that end, we extend a recently proposed Deep Alignment Network (https://github.com/MarekKowalski/DeepAlignmentNetwork) with a term related to facial features. Thanks to this simple modification, our model called EmotionalDAN is able to outperform state-of-the-art emotion classification methods on two challenging benchmark dataset by up to 5%. Furthermore, we visualize image regions analyzed by the network when making a decision and the results indicate that our EmotionalDAN model is able to correctly identify facial landmarks responsible for expressing the emotions.
To run the code you need:
- Tensorflow 1.8 or newer
- OpenCV 3.1.0 or newer
Some parts of the code use the following Tensorflow implementation of DAN: https://github.com/mariolew/Deep-Alignment-Network-tensorflow
To read the training data run the function from DataSetPre.py file:
ReadList('train.txt', 'Trainset', isTrainSet=True), where train.txt is in the following format:
path_to_jpg_image class_label
If you use this software in your research, then please cite one of the following papers:
I. Tautkute and T. Trzcinski : Classifying and Visualizing Emotions with Emotional DAN
I. Tautkute and T. Trzcinski and A. Bielski: I Know How You Feel: Emotion Recognition with Facial Landmarks, CVPRW 2018
If you have any questions or suggestions feel free to contact me at [email protected].