Robust Low-Quality Emotion Recognition
This is codes for Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach
Requirements
- compile CAFFE and matcaffe
- install MATLAB
Usage
Download the data package for the Multimodal Affect Recognition Sub-Challenge (MASC) of the 6th Audio/Visual Emotion Challenge and Workshop (AVEC 2016): "Depression, Mood and Emotion".
-
Use
./matlabscripts/prepare_dataset.m
to generate cropped faces. -
Use
./matlabscripts/generate_h5/prepare_h5_files.m
to generate HDF5 files for training -
Use network
./experiments/network/model_1F_CNN+D.prototxt
and solver./experiments/solver/solver_1F_CNN+D.prototxt
for HQ and LQ -
Use
./pretrain/generate_train.m
and./pretrain/generate_test.m
to generate HDF5 files for SR model -
Use
./pretrain/Pretrain_solver.prototxt
to train SR model -
Use
./experiments/network/model_1F_CNN+D_vlqr_non_joint.prototxt
and./experiments/solver/solver_1F_CNN+D_vlqr_non_joint.prototxt
for LQ-non-joint -
Use
./experiments/network/model_1F_CNN+D_vlqr.prototxt
and./experiments/solver/solver_1F_CNN+D_vlqr.prototxt
for vlqr
Pretrain Model
You can download the pretrained model from Dropbox
Citation
If you use this code for research, please cite our papers:
@article{cheng2017robust,
title={Robust emotion recognition from low quality and low bit rate video: A deep learning approach},
author={Cheng, Bowen and Wang, Zhangyang and Zhang, Zhaobin and Li, Zhu and Liu, Ding and Yang, Jianchao and Huang, Shuai and Huang, Thomas S},
journal={arXiv preprint arXiv:1709.03126},
year={2017}
}
@article{liu2017enhance,
title={Enhance Visual Recognition under Adverse Conditions via Deep Networks},
author={Liu, Ding and Cheng, Bowen and Wang, Zhangyang and Zhang, Haichao and Huang, Thomas S},
journal={arXiv preprint arXiv:1712.07732},
year={2017}
}