Emotion-detection

Introduction

This project aims to classify the emotion on a person's face into one of seven categories, using deep convolutional neural networks. This algorithm is based on the research paper attached.

Compatibility

• This code has been tested on Ubuntu 16.04 LTS and is most likely compatible on all platforms.

• Dependencies: Python 3.5+, OpenCV 3.0, TFlearn.

Usage

• Clone the repository and download the following files(trained models) from these links :

  • data
  • index
  • meta

• To run the program to detect emotions only in one face, type python em_model.py singleface.

• To run the program to detect emotions on all faces close to camera, type python em_model.py multiface.

Algorithm

• First, we use haar cascade to detect faces in each frame of the webcam feed.

• The region of image containing the face is resized to 48x48 and is passed as input to the ConvNet.

• The network outputs a list of softmax scores for the seven classes.

• The emotion with maximum score is displayed.

Example Output

References

• Emotion-recognition-and-prediction

• Emotion-recognition-neural-networks