Facial Expression Recognition Using Machine Learning Techniques

Abstract

Emotion recognition from facial expression is an exciting field of research with applications like safety, security, personal information and marketing. Researchers want to develop techniques that can interpret, and extract facial expressions so that computers can make better emotional predictions. In recent years, different types of architectures have been used in machine learning to improve facial expression performance. In this paper, machine learning techniques are used to study facial emotion recognition. We present various machine learning techniques to identify the best methodology for the test at hand. Support Vector Machine (SVM), Convolution Neural Network (CNN) and Artificial Neural Network (ANN) along with face detection and preprocessing techniques for the expressions in Japanese Female Facial Expression (JAFFE) dataset and the Extended Cohn-Kanade (CK+) dataset are exploited to achieve best accuracy of 98.47% on CK+ dataset using CNN, and 89.18% accuracy for JAFFE dataset using ANN.