dliofindia/facial-emotion-recognition-using-machine-learning

Facial Emotion Recognition using Machine Learning

Getting Started

Emotion detection has become a topic of continuous research and innovation as over the past decade the limitations of computer vision have been lifted by the introduction of machine learning. As machine learning algorithms leverage the huge computation power of GPU’s, the image processing capabilities of these models fit suitably with real world problems. Computer vision has spread from a novel domain to several other domains such as the behavioral sciences. These algorithms or models are being used in various real world applications across several fields such as security, driver-safety, autonomous vehicles, human-computer interaction and healthcare. These models are continuously evolving due to the introduction of graphical processing units which are hardware equipments capable of performing millions of computations within mere seconds or minutes. The emergence of technologies such as augmented reality and virtual reality are also dependent on these GPU’s heavily. Hence, combining the statistical intelligence of machine learning algorithms and the processing power of GPU hardware, we are able to create breakthrough models capable of detecting emotions from static images as well as video feeds.

The project work entitled "Facial Emotion Detection Using Machine Learning" would envisage the following:

1. Capturing static images for pre-processing.
2. Feature Extraction using Deep Neural Networks.
3. Implementing a convolutional neural network to classify images based on the aforementioned features

Dataset

We are using the Cohn-Kanade dataset available here http://www.consortium.ri.cmu.edu/ckagree/. The dataset we used for training our model is an augmented version of the CK+ dataset.

This is done so as to increase the number of samples for training our model.The total size of our dataset is 2536 samples.

The model is based on the Keras library on a Tensorflow Backend. But, if Tensorflow is not available, Theano can be used instead by changing just one line in the code:

from keras import backend as K
K.set_image_dim_ordering('tf')      #('th')

Change the 'tf' to 'th' to use the Theano backend. The rest of the code is structured to handle both the types of backends.

Requirements

• Python 3.5 or above
• Tensorflow 1.6
• Keras 2.1.0
• OpenCV
• scikit-learn
• Numpy
• Matplotlib
• itertools

Training the model

Install the required libraries and run this file. Training takes a while to complete, but it can be sped up by using GPU options.

python fred_train.py

Running Test

To test the model make sure your webcam is working or attach an external webcam is online.

python facialmore.py

To test the model on static images run:

python fredtest.py

You can use the fredtest_data dataset provided with the code.

Results

• Threshold Accuracy: 86.98%
• Test Loss: 1.868 Validation Loss: 0.973
• Test Accuracy:0.705 Validation Accuracy:0.721
• Confusion Matrix

The model performs pretty well considering we are not using any GPU's in this project. Although using GPU's would yield a much better result. The erratic behaviour of the loss and accuracy graphs is due to loss of features pertaining to no presence of GPU's. Also keep in mind that while testing the model with live feed, setup an ideal environment with good lighting and a proper webcam.