Skip to content

a TensorFlow implementation of the paper "Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Images"

Notifications You must be signed in to change notification settings

nanfangAlan/FSRFER

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

8 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

FSR-FER (TensorFlow)

This repository provides a TensorFlow implementation of the paper "Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Images" More details will be added in the future.

Dependencies

tensorflow, openCV, sklearn, numpy

The versions of my test environment :
Python==3.6.8, tensorflow-gpu==1.12.0, openCV==4.1.0, scikit-learn==0.20.3, numpy==1.16.2

Models

You can download following models:

  • The CovPoolFER models were used as the facial expression recognition model in our paper. You could download the pretrained models in this depository and unzip them in the project, make sure the dir has pretrained CovPoolFER model path "./models/model2/20170815-144407"
  • The pretrained FSR-FER model will be added in the future.

Datasets

The LR images were downsampled and resized to 100x100 with imresize() function in Matlab. Append "x2-x8" to the filename and put LR images in the same dir. The HR and LR dataset path should be like this:

**+--RAFDB_train_lr**
|-- minitest
|   |-- Angry
|        |-- test_0027x2.jpg
|        |-- test_0027x3.jpg
|        |-- test_0027x4.jpg
|        |-- test_0027x5.jpg
|        |-- test_0027x6.jpg
|        |-- test_0027x7.jpg
|        |-- test_0027x8.jpg
|        |-- test_0037x2.jpg
|        \`-- ...
|   |-- Disgust
|   |-- Fear
|   |-- Happy
|   |-- Neutral
|   |-- Sad
|   \`-- Surprise

**+--RAFDB_train_hr**
|-- test_100
|   |-- Angry
|        |-- test_0027.jpg
|        |-- test_0037.jpg
|        |-- test_0047.jpg
|        \`-- ...
|   |-- Disgust
|   |-- Fear
|   |-- Happy
|   |-- Neutral
|   |-- Sad
|   \`-- Surprise

Train and Test

Use train.sh to train the FSRFER model and load the trained model with classify.sh to train the classifier and classify features.

|-- classify
|   |-- classify.sh                    
|   |-- classify_FER.py         (train and test the FER classifier)
|   |-- classify_FSRFER.py      (train and test the FSRFER classifier)
|   \`-- framework.py
|-- data
|-- lib
|   |-- covpoolnet.py
|   |-- network.py
|   |-- ops.py
|   |-- pretrain_generator.py
|   |-- train_module_full.py
|   \`-- utils.py
|-- nohup.out
|-- train.py                    (train the FSRFER feature extractor)
\`-- train.sh

About

a TensorFlow implementation of the paper "Feature Super-Resolution Based Facial Expression Recognition for Multi-scale Low-Resolution Images"

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published