The dsn from amirunpri2018

dsn's Introduction

Video-based Emotion Recognition Using Deeply-Supervised Neural Networks

An ensemble of proposed models achieves an accuracy of 61.10% in EmotiW 2018. Details about the EmotiW2016 Challenge can be found at: https://sites.google.com/view/emotiw2018. For more details about the codes, please refer to our paper.

Model accuracy on the validation set:

Accuracy of our top 4 submissions to EmotiW 2018:

Requirements

pycaffe
python 2.7
ffpemg
opencv 2.4.11
cuda 8.0

Datasets and models

Datasets:

Two datasets we used can be downloaded from the EmotiW 2018 and Real-world Affective FacesRAF-DB. You can send an e-mail to the author to access the database.
Data Pre-processing:

(1) Extract all frames of the videos:
```
cd ./scripts 
python extract_frames.py  
```
(2) We employe MTCNN implemented in facenet to detect faces in the frames (image size=400，margin=100).

(3) Compile Dlib's Python interface and download shape_predictor_68_face_landmarks.dat. Get 68 landmarks of 400*400 images cropped by MTCNN.
```
cd ./scripts 
wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
python getLandmarks.py
```
(4) Face alignments from the cropped images. landmark_list.txt is a list of 68 landmarks detected by Dlib; /CropData/ is the directory of 400*400 cropped images; /AlignData/ is the target directory of aligned faces.
```
./face_align  landmark_list.txt  ./CropData/  ./AlignData/
```
Models:

To train DSN-VGG-FACE , DSN-Res-50 or DenseNet-121,
you can finetune using pretrained models from: VGG-FACE,ResNet-50,DenseNet-121.
```
git clone https://github.com/EvelynFan/DSN.git  
cd ./DSN-VGG-FACE  
python run_dsn.py  
cd ./DSN-Res-50  
python run_dsn_res50.py
```
To test on the validation set and test set of 2018 Emotion Challenge Dataset, please download our models from GoogleDrive
```
cd ./scripts 
python test_video.py  
```
Model fusion:
```
cd ./scripts 
python merge_score.py
```