Facial Expression Recognition

Software

Pycharm

Packages

1) Keras

2) Tensorflow

3) OpenCV

4) Numpy

Features

• Live Face Detection
• Live Face Expression Tracker

To Run The Project

  Import the 'Training_Model.py', 'Face_Recognition.py', 'haarcascade_frontalface_default.xml', 'Train' & 'Validation' Folder into your Project Directory.

  Install all the Required Packages via the Python Interpreter Settings in PyCharm

  Open Training_Model.py and Run the File to generate the Emotion_Checkpoint.h5, which is basically a file which stores all the training data points which has the best 
  accuracy and least error after going through the specified number of training rounds.

  Now, Open the Face_Recognition.py and Run the File. Finally, a webcam will open and the program hopefully will be able to detect your facial expressions to its level best.

NOTE (To Enable GPU when Training Datasets)

  To fasten the process of generating the Emotion_Checkpoint.h5, it is recommended to use the system GPU, if applicable.

  Install NVIDIA CUDA Toolkit and verify if the system has CUDA capable GPU.

  Next, install the Deep Neural Network library NVIDIA CuDNN using the guide provided below in 'Resources' section.

  Once, both CUDA and CuDNN are installed. Open Pycharm and Goto: File ---> Settings ---> Project Interpreter

  Next, Select the appropriate Environment which has TensorFlow-GPU installed and Select: Run ---> Edit Configuration ---> Environment Variables

  Finally, add the lib path as 'Some Name'_PATH 

Training Sample Screenshots

Demo

Code Sample Screenshots

Resources

• https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#install-windows
  
• https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data
  
• https://pythonprogramming.net/haar-cascade-face-eye-detection-python-opencv-tutorial/