Emotion recognition using image processing

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Emotion recognition is an active field of research that has seen a lot of interest over the past decade. Historically, people’s emotions were analysed and determined through human observation and psychological counselling and later evolved to electrophysiological and largely intrusive methods such as Electroencephalography (EEG) because of how complex of a task it is and how extensive its application could be. Currently, with the entrance of machine learning and computer vision-related technologies, computers and robots can now be trained to learn and predict the emotions of human beings either in real-time or with their static facial images. In this research, emotion recognition is explored with respect to its three widely recognised stages: face detection, feature extraction and emotion recognition. At each of these stages, different image processing methods and learning techniques are explored and tested. A Convolutional Neural Network was trained and tested and recorded an accuracy of 50.7%. A Support Vector Machine was also trained and tested and recorded an accuracy of 81.5%. Both classifiers were trained on 7 emotion categories. The results show that it is possible for computers to predict the emotions of humans using image processing techniques and deep learning models. ; deep learning; ; image processing;
Undergraduate thesis submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, May 2020
emotion recognition, machine learning, Convotional Neural Network, Support Vector Machine