LookSense - A Facial Expression Recognition

Introduction

Employing deep convolutional neural networks, this project seeks to categorize facial expressions into seven distinct emotions. Trained on the FER-2013 dataset, featured at the International Conference on Machine Learning (ICML), the dataset encompasses 35,887 grayscale images of 48x48 dimensions, each portraying one of seven emotions: anger, disgust, fear, happiness, neutrality, sadness, and surprise.

Dependencies

• Python 3, OpenCV, TensorFlow
• To install the required packages, run pip inistall -r requirements.txt.

Basic Usage

The repository is currently compatible with tensorflow-2.0 and makes use of the Keras API using the tensorflow.keras library.

• First, clone the repository and enter the folder

  git clone https://github.com/sudoshivesh/LookSense.git
  cd LookSense

• Download the FER-2013 dataset inside the src folder.

• If you want to train this model, use:

  cd src
  python emotions.py --mode train

• If you want to view the prediction without training again, I have already given a pre-trained model inside the src folder named as LookSense/src/model.h5 then run the command:

  cd src
  python emotions.py --mode display

*The folder structure is of the form: src:

• data (folder)

• dataset_prepare.py (file)

• emotions.py (file)

• haarcascade_frontalface_default.xml (file)

• model.h5 (file)

• By default, the system can recognize emotions on multiple faces within a live webcam stream. Achieving a test accuracy of 63.2% over 50 epochs, the implementation utilizes a straightforward 4-layer convolutional neural network (CNN) for efficient emotion detection.

• The snapshot of the output is given below:

  

  

• For any query, You may conatct Shivesh