MatConvNet-based MATLAB implementation for
- [Challenge] Winner of SFEW Part, The 3rd Emotion Recognition in the Wild Challenge (EmotiW’15), 2015
- [Paper] "Hierarchical Committee of Deep Convolutional Neural Networks for Robust Facial Expression Recognition," Journal on Multimodal User Interfaces (JMUI), 2016
- Forder
lib1_AlignFace_NormalizeInput
include- codes for face registration (multi-pipeline-based alignment)
- codes for input normalization (illumination normalization, contrast enhancement) + input matrix (imdb) formation for MatConvNet toolbox
- Download the following libraries for face registration
/pipeline_modules_functions/module1_ZR_FaceDetector
- visit https://www.ics.uci.edu/~xzhu/face/ → download and unzip "face-release1.0-basic.zip" → move the unzipped files to "module1_ZR_FaceDetector"
/pipeline_modules_functions/module3_INTRAFACE_LandmarkDetector
- visit http://humansensing.cs.cmu.edu/intraface/download_functions_matlab.html → download and unzip "FacialFeatureDetection&Tracking_v1.4.0.zip" → move the files to "module3_INTRAFACE_LandmarkDetector"
- Forder
lib2_TrainDeepCNN
includes- codes to train individual CNNs
- based on MatConvNet toolbox (version1.0-beta8)
- codes to train individual CNNs
- Forder
lib3_HierarchicalCommittee
includes- codes to form hierarchical committee of CNNs
- single-level & two-level committees
- codes to form hierarchical committee of CNNs
- Due to memory and license issues, we cannot entirely provide the datasets (SFEW2.0, FER-2013, TFD) and all the trained models (240 CNNs) used in our paper.
- However, this repository includes executable codes for some sample data. We believe that you can obtain the results in our paper if you use full data and train whole models.
If you plan to use our codes, please consider citing our paper:
@article{kim2016hierarchical,
title={Hierarchical committee of deep convolutional neural networks for robust facial expression recognition},
author={Kim, Bo-Kyeong and Roh, Jihyeon and Dong, Suh-Yeon and Lee, Soo-Young},
journal={Journal on Multimodal User Interfaces},
volume={10},
number={2},
pages={173--189},
year={2016},
publisher={Springer}
}
@inproceedings{kim2015hierarchical,
title={Hierarchical committee of deep cnns with exponentially-weighted decision fusion for static facial expression recognition},
author={Kim, Bo-Kyeong and Lee, Hwaran and Roh, Jihyeon and Lee, Soo-Young},
booktitle={Proceedings of the 2015 ACM on International Conference on Multimodal Interaction},
pages={427--434},
year={2015}
}