Facial Expression Classification using KNN and Decision Tree Classifiers

Abstract— In recent years, facial emotional expression
recognition attracts several researchers for developing
intelligent human-machine interface (HMI) system. This
present work classify six different facial expressions
(happiness, sadness, anger, fear, disgust, and surprise) usingtwo classifiers namely, K Nearest Neighbor (KNN) and
Decision Tree (DT) classifiers. Fifty-five undergraduate
university students (35 male and 20 female) with a mean age of23.9 years voluntarily participated in the experiment to acquire
six facial emotional expressions using ten virtual markers
called Facial Action Units (FAUs). Firstly, Haar-like featuresare used for detecting the face and eyes in a video-frame usingViola-Jones adaboost classification method. These FAUs areplaced on specific location on the subject’s face based on facialaction coding system (FACS) using a mathematical model.Lucas-Kande optical flow algorithm is used to continuouslytrack the markers positions. Here, the distance between theFAU at the center of the subject face to other markers