fer

Facial Emotion Recognition (FER)

This work is published on arXiv

Our final model checkpoint can be found here

Overview

In this work, we achieve the highest single-network classification accuracy on FER2013. We adopt the VGGNet architecture, rigorously fine-tune its hyperparameters, and experiment with various optimization methods. To our best knowledge, our model achieves state-of-the-art single-network accuracy of 73.28 % on FER2013 without using extra training data.

Architecture

Tuning

In tuning, we experiment with several deifferent optimizers, learning schedulers and run a grid search over all parameters. Some of our results are shown below.

Optimizers	Schedulers

Confusion Matrix

Saliency Maps

Visualizing the information captured inside deep neural networks helps describe how they differentiate between different facial emotions. A saliency map is a common technique used in visualizing deep neural networks. By propagating the loss back to the pixel values, a saliency map can highlight the pixels which have the most impact on the loss value. It highlights the visual features the CNN cancapture from the input; thus, allowing us to better understand the importance of each feature in the original image on the final classification decision.

Installation

To use this repo, create a conda environment using environment.yml or requirements.txt

# from environment.yml (recommended)
conda env create -f environment.yml

# from requirements.txt
conda create --name <env> --file requirements.txt

Download the offical fer2013 dataset, and place it in the outmost folder with the following folder structure datasets/fer2013/fer2013.csv

Usage

To train your own version of our network, run the following

python train.py network=vgg name=my_vgg

To change the default parameters, you may also add arguments such as bs=128 or lr=0.1. For more details, please refer to utils/hparams.py