Comparative analysis of machine learning KNN, SVM, and random forests algorithm for facial expression classification

Human depicts their emotions through facial expression or their way of speech. In order to make this process possible for a machine, a training mechanism is needed to give machine the ability to recognize human expression. This paper compare and analyse the performance of three machine learning algorithm to do the task of classifying human facial expression. The total of 23 variables calculated from the distance of facial features are used as the input for the classification process, with the output of seven categories, such as: angry, disgust, fear, happy, neutral, sad, and surprise. Some test cases were made to test the system, in which each test cases has different amount of data, ranging from 165-520 training data. The result for each algorithm is quite satisfying with the accuracy of 75.15% for K-Nearest Neighbor (KNN), 80% for Support Vector Machine (SVM), and 76.97% for Random Forests algorithm, tested using test case with the smallest amount of data. As for the result using the largest amount of data, the accuracy is 98.85% for KNN, 90% for SVM, and 98.85% for Random Forests algorithm. The training data for each test case was also classified using Discriminant Analysis with the result 97.7% accuracy.