/Facial-Expression-Recognition-with-CNNs

Facial Expression Recognition with CNNs on TensorFlow-Keras with OpenCV and Python.

Primary LanguageJupyter NotebookMIT LicenseMIT

License: MIT

Facial-Expression-Recognition-with-CNNs

Facial Expression Recognition with CNNs on TensorFlow-Keras with OpenCV and Python. Flask app was used to get a web-interface to deploy the algorithm.

Video source: https://www.youtube.com/watch?v=5w3cYtJekpw

Algorithm output: https://youtu.be/ojB1LSCKUpM

Video source: https://www.youtube.com/watch?v=B0ouAnmsO1Y

Algorithm output: https://youtu.be/jzaEGQrXRtA

Overview

In this project, I built and trained a convolutional neural network (CNN) in Keras from scratch to recognize facial expressions. I have used the 2013 Facial Emotion Recognition Dataset (https://datarepository.wolframcloud.com/resources/FER-2013). The dataset consists of 48x48 pixel grayscale images of faces. The objective is to classify each face based on the emotion shown in the facial expression into one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral). I used OpenCV to automatically detect faces in images and draw bounding boxes around them. Once the model has been trained, saved, and exported the CNN, I use the trained model to a web interface and perform real-time facial expression recognition on video and image data. The video input can be changed to take in webcam by uncommenting one line of code. Real time perfomance depends on the hardware specifications.

Project Requirements/ Dependencies

TensorFlow-GPU

OpenCV

Seaborn

Matplotlib

Keras

Livelossplot

Flask

CNN model architecture

Pipeline

  1. Take the video stream and convert it to frames. Use Haar cascades to identify and draw bounding box around faces.

  2. Pass the Region of Interest (Face patch) to the pretrained model. Use Forward feed to detect the expression.

  3. Take the output of the model and add the label to the image and return the frame as a 'jpeg' file.

  4. Use flask to publish the resulting frames onto the website. (HTML file has the template for video output) The output is deployed on 'http://localhost:5000/' web address.

Results

Accuracy and Loss Plots 15 epochs

Accuracy and Loss Plots for 25 epochs

Accuracy and Loss Plots for 50 epochs

Command to run code

python main.py

Certification

Coursera certificate for the project: https://coursera.org/share/cb5b1cee88ad5ded6055c0f0c3adeaa4

Known Issues/Bugs

The Haar Cascades are not the most robust way of identifying faces. Observed a few false face detections at times near a person's neck. A better approach would be to use DLib library for a faster and more accurate face detection. (http://dlib.net/) Would be working on Dlib library for future projects.

The data in the 'Disgust' class can be augmented to get a more generalized model.

References

Coursera: https://www.coursera.org/projects/facial-expression-recognition-keras

Challenges in Representation Learning: https://arxiv.org/pdf/1307.0414.pdf

Licence

The Repository is Licensed under the MIT License.

MIT License

Copyright (c) 2020 Nagireddi Jagadesh Nischal

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