Person-Dependent and Person-Independent Analysis of Emotion Recognition using Facial Expressions

Facial emotion recognition is one of the prospective fields which can have various applications in many different areas. However, there is a huge difference between a personalized and non-personalized emotion recognition. In facial expression analysis, learning process starts with person’s facial structure. A person-dependent system will receive person specific features during training which is advantageous compared to a person-independent system. Hence, with the addition of ethnicity, cultural background or gender differences, gathering results on non-personalized system of emotion recognition becomes a challenge. In this thesis, models for person-dependent and person-independent emotion recognition are proposed. Experiments are carried out using SAVEE and RML facial video databases. Initially, frames and corresponding landmark features are extracted from the videos. K-means clustering algorithm is applied to the extracted landmark features in order to get the k most significant frames. After representing each video sequence with k keyframes, Support Vector Machine classifier is used for the training and testing of the proposed system. Experimental results show that recognition performance of person-dependent model is higher than person-independent model. Keywords: Machine Learning; Image Analysis; Emotion Recognition; Facial Emotion Recognition; Support Vector Machine