DAM-CNN

the demo code of model DAM-CNN

Brief Introduction

This code is corresponding to the paper: Xie, Siyue, Haifeng Hu, and Yongbo Wu. "Deep Multi-path Convolutional Neural Network Joint with Salient Region Attention for Facial Expression Recognition." Pattern Recognition (2019).

In this code, I just exemplify the training and validation/testing process of DAM-CNN on FER2013 dataset. (Because only FER2013 is open-access and all other datasets mentioned in the paper should be licenced before using them.)

Enviornment Requirements

• Python 3.4
• TensorFlow 1.2-gpu
• Numpy
• Scipy

Setup

• I have appended the pre-processed FER2013 in the file 'DB.rar'. Just extract DB.rar in the current directory. You can access to the original FER2013 in https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data
• The model needs to load the pretrained VGG-Face model. The parameters of VGG-Face (in the form of .npy) can be downloaded here. Just place it in the current directory. The original pretrained VGG-Face model can be downloaded here

Other Information

• In the paper, DAM-CNN is trained following a two-stage strategy. In this code, I have improved it so the model can be trained in an end-to-end manner. The training strategy of this code is a little different with the description in our paper. But the classification results of this code are consistent with the accuracy we reported in the paper.
• The model gets benefit from the code of MTAE.