Emotion-Recognition-using-Facial-Landmarks

This project is made for recognizing Human facial expressions without using convolutional neural nets. It uses non feature learning approaches such as Facial Landmarks.The best accuracy for Fer2013 (as I know) is 67%, the author trained a Convolutional Neural Network during several hours in a powerful GPU to obtain this results.Here is a much simpler (and faster) approach by extracting Face Landmarks and feeding them to a multi-class SVM/ Logistic Regression/ Randomforest classifier.

Requirements

You will need the following to run the above:

• Python3.5
• Numpy
• OpenCV
• SKLearn
• Dlib

How to run the code

1. • Download the 'fer2013' dataset from here
   • run the following script to convert the .csv file into images

$ python3 csv_to_images.py -f <path to the fer2013.csv file> -o <output folder to save images in>

2. Download the landmarks file from here

3. Train the model by running the following in the terminal

$ python3 train.py

Results

Model	Accuracy
LogisticRegression	55.1%
RandomForest	54%

Note: While the training time is very short compared to CNN, we lost much of the accuracy compared to the actual best result that uses CNN.