Facial action unit intensity estimation using rotation invariant features and regression analysis

There has been quite a lot of research done in the field of Facial Expression Recognition, yet there has not been so much development in Facial Action Coding System Action Unit intensity detection. In Automated Facial Expression Recognition, intensity recognition of the Facial Action Coding System Action Units is a crucial part for it would give much broad information about the facial expression of an individual. In this research, a computationally efficient yet effective logistic regression based method that operates on a novel feature vector extracted from geometric relations between facial feature points is presented. Said method uses angles between facial feature points which are rotation invariant. The method was trained and tested on DISFA database and gave state of the art results.