End-to-End Multimodal Emotion Recognition using Deep Neural Networks
This package provides training and evaluation code for the end-to-end multimodal emotion recognition paper. If you use this codebase in your experiments please cite:
P. Tzirakis, G. Trigeorgis, M. A. Nicolaou, B. Schuller and S. Zafeiriou, "End-to-End Multimodal Emotion Recognition using Deep Neural Networks," in IEEE Journal of Selected Topics in Signal Processing, vol. PP, no. 99, pp. 1-1. (http://ieeexplore.ieee.org/document/8070966/)
UPDATE
Implementation of this method in PyTorch (along with pretrain models) can be found in our End2You toolkit
Requirements
Below are listed the required modules to run the code.
- Python <= 2.7
- NumPy >= 1.11.1
- TensorFlow <= 0.12
- Menpo >= 0.6.2
- MoviePy >= 0.2.2.11
Content
This repository contains the files:
- model.py: contains the audio and video networks.
- emotion_train.py: is in charge of training.
- emotion_eval.py: is in charge of evaluating.
- data_provider.py: provides the data.
- data_generator.py: creates the tfrecords from '.wav' files
- metrics.py: contains the concordance metric used for evaluation.
- losses.py: contains the loss function of the training.
- inception_processing.py: provides functions for visual regularization.
The multimodal model can be downloaded from here : https://www.doc.ic.ac.uk/~pt511/emotion_recognition_model.zip