#This is the source code for Chalearn LAP Real Versus Fake Expressed Emotion Challenge @ICCV 2017
Author: Huynh Xuan Phung
Email: phunghx <at> gmail <dot> com

Title: A LSTM network with Parametric Bias and its Application to Real versus Fake Emotion Recognition
Author: Xuan-Phung Huynh
HCI Lab, Sejong University, Korea

• Dependency libraries:

0. Ubuntu 16.04
1. Torch 7: http://torch.ch/docs/getting-started.html
2. Python 2.7
3. OpenCV 2.4.9
4. Xgboost https://github.com/dmlc/xgboost
5. cunn library for torch
6. npy4th https://github.com/htwaijry/npy4th
7. Cuda 8.0

• Instructions:

1. Clone this repository into your local machine: https://github.com/phunghx/Real_Fake_Expression. I set this reposity on your machine is REALFAKE=/Real_Fake_Expression.

2. Dataset: there are 4 ziped files as following:(link https://sejonguniversity-my.sharepoint.com/personal/phunghx_sju_ac_kr/_layouts/15/guestaccess.aspx?folderid=0b6feb31128fa428b82c54fc412f64d1a&authkey=AaVIHwtz95X0uCG_wve7Qz8 )

   • Train.zip: 40 subjects for training that include face region only. Please download this file if you want to train the LSTMPB models
   • testImage.zip: face regions for testing video.
   • validImage.zip: face regions for validation video
   • shapeTrain.zip: face landmarks of training videos that we use for training xgboost models
   • shapeTest.zip: face landmarks of testing videos for our submission on challenge
   • shapeValid.zip: face landmarks of validating videos for submission on challenge

3. Build and install dependence libraris:

   • cuda 8.0
   • g++ 5.4
   • torch 7.1
   • python 2.7
   • opencv 2.4.9 for python
   • xgboost : sudo pip install xgboost
   • sklean: sudp pip install sklearn --upgrade
   • scikit-image: sudo pip install scikit-image --upgrade
   • libjpeg: sudo apt-get install libjpeg-dev
   • build dlib. From REALFAKE folder, run ./buildLibs.sh <torch installed folder >. For example, I install my torch at /libs/torch then I run ./buildLibs.sh /libs/torch/install

4. Replicate our results on the challenge website

   • delete all folders and files in REALFAKE/data
   • clear data ./cleardata.sh
   • download testImage.zip and extract to REALFAKE/data (REALFAKE/data/testImage)
   • download shapeTest.zip and extract to REALFAKE/data (REALFAKE/data/shapeTest)
   • run ./testing_data.sh REALFAKE/data/shapeTest REALFAKE/data/testImage
   • Final result is the file test_prediction.pkl in REALFAKE folder
   • If you want to see the result on the validation set, please download the validImage.zip and shapeValid.zip

5. Training

   • download and extract Train.zip into your machine. I assume the path of your extraction is /data/Train which contains 40 folders for 40 subjects.
   • Train LSTMPB : ./training.sh /data/Train
   • Generate landmark for training data: you can download shapeTrain.zip and extract to REALFAKE/data/shapeTrain (contain of 40 folders) or run ./createlandmarkData.sh /data/Train
   • Genetate PB vectors for training xgboost: ./createTrainData.sh REALFAKE/data/shapeTrain
   • Training xgboost: ./training_xgboost.sh

6. Testing

   • Copy all video into a folder, we set it at $REALFAKE/data/test. The name of video is followed the challenge dataset: <id>_<facial>.mp4. <facial> is one of ANGER,CONTENTMENT,DISGUST,HAPPINESS,SADNESS,SURPRISE.
   • extract face regions from testing video: ./extractFace.sh $REALFAKE/data/test
   • testing: ./testing.sh REALFAKE/data/shapeTest REALFAKE/data/testImage
   • Final result is the file test_prediction.pkl in REALFAKE folder

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Please read our paper: Discrimination between genuine versus fake emotion using long-short term memory with parametric bias and facial landmarks