Data-Driven Facial Expression Analysis from Live Video

Emotion analytics is the study of human behavior by analyzing the responses when humans experience different emotions. In this thesis, we research into emotion analytics solutions using computer vision to detect emotions from facial expressions automatically using live video. Considering anxiety is an emotion that can lead to more serious conditions like anxiety disorders and depression, we propose 2 hypotheses to detect anxiety from facial expressions. One hypothesis is that the complex emotion “anxiety” is a subset of the basic emotion “fear”. The other hypothesis is that anxiety can be distinguished from fear by differences in head and eye motion. We test the first hypothesis by implementing a basic emotions detector based on facial action coding system (FACS) to detect fear from videos of anxious faces. When we discover that this is not as accurate as we would like, an alternative solution based on Gabor filters is implemented. A comparison is done between the solutions and the Gabor-based solution is found to be inferior. The second hypothesis is tested by using scatter graphs and statistical analysis of the head and eye motions of videos for fear and anxiety expressions. It is found that head pitch has significant differences between fear and anxiety. As a conclusion to the thesis, we implement a systems software using the basic emotions detector based on FACS and evaluate the software by comparing commercials using emotions detected from facial expressions of viewers.