# ParameterFreeRCNs-MicroExpressionRec

**Repository Path**: xia_zhaoqiang/ParameterFreeRCNs-MicroExpressionRec

## Basic Information

- **Project Name**: ParameterFreeRCNs-MicroExpressionRec
- **Description**: This repository contains the source code for our TIP work, which is entitled as "Revealing the Invisible With Model and Data Shrinking for Composite-Database Micro-Expression Recognition".
- **Primary Language**: Python
- **License**: CC-BY-4.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-12-31
- **Last Updated**: 2021-12-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: MER

## README

Recurrent convolutional networks with parameter free modules for composite-database micro-expression recognition
Descriptions

These codes are used for micro-expression recognition on composite datasets (e.g., MEGC2019). The methods can be accessed by the paper "Revealing the Invisible With Model and Data Shrinking for Composite-Database Micro-Expression Recognition, IEEE TIP2020", which includes the RCN-A, RCN-S, RCN-W, RCN-P, RCN-C and RCN-F.
Dependencies

The code was written in Python 3.6, and tested on Windows 10 and CentOS 7.

    Pytorch: 1.1 or newer
    Numpy: 1.16.3 or newer
    Scikit-learn: 0.22.1 or newer

Instructions

    The optical flow should be extracted by your own tools before training the deep model.
    The data can be prepared by the script "PrepareData_LOSO_CD.py".
    At last, various models can be accessed by using the commond "--modelname". For training, the command like "python ModelEval_Final.py --dataset smic --dataversion 1 --epochs 20 --learningrate 0.0005 --modelname rcn_a --batchsize 64 --featuremap 32 --poolsize 5 --lossfunction crossentropy" can be used.