Front cover image for Measuring the intensity of spontaneous facial action units with dynamic Bayesian network


Measuring the intensity of spontaneous facial action units with dynamic Bayesian network

Automatic facial expression analysis has received great attention in different applications over the last two decades. Facial Action Coding System (FACS), which describes all possible facial expressions based on a set of facial muscle movements called Action Unit (AU), has been used extensively to model and analyze facial expressions. FACS describes methods for coding the intensity of AUs, and AU intensity measurement is important in some studies in behavioral science and developmental psychology. However, in majority of the existing studies in the area of facial expression recognition, the focus has been on basic expression recognition or facial action unit detection. There are very few investigations on measuring the intensity of spontaneous facial actions. In addition, the few studies on AU intensity recognition usually try to measure the intensity of facial actions statically and individually, ignoring the dependencies among multilevel AU intensities as well as the temporal information. However, these spatiotemporal interactions among facial actions are crucial for understanding and analyzing spontaneous facial expressions, since these coherent, coordinated, and synchronized interactions are that produce a meaningful facial display. In this paper, we propose a framework based on Dynamic Bayesian Network (DBN) to systematically model the dynamic and semantic relationships among multilevel AU intensities. Given the extracted image observations, the AU intensity recognition is accomplished through probabilistic inference by systematically integrating the image observations with the proposed DBN model. Experiments on Denver Intensity of Spontaneous Facial Action (DISFA) database demonstrate the superiority of our method over single image-driven methods in AU intensity measurement.
Highlights: • This paper aims to measure the intensity of spontaneous facial action unit. • The proposed framework models the correlation of AU intensities to improve the recognition accuracy. • Advanced machine learning methods are introduced to learn the parameters of the proposed model. • Plenty of experiments on DISFA database demonstrate the effectiveness of the proposed method
Article, 2015
Pattern Recognition, 48, 201511, 3417
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