Temporal Facial Expression Modeling for Automated Action Unit Intensity Measurement
Spontaneous facial expression recognition using temporal patterns is a relatively unexplored area in facial image analysis. Several factors such as head orientation, co-occurrence and presence of subtle facial action units (AUs), and time variability of AUs make the problem more challenging. This paper presents a methodology to model and automatically recognize the intensity of spontaneous AUs in videos. Our method exploits localized Gabor features and Hidden Markov Model (HMM) to represent and model the dependencies of AU dynamics in both subject-dependent (SD) and subject-independent (SI) settings. Our experimental results show that temporal information can improve the recognition of AUs and their intensity levels compared to static methods.