Support Vector Regression of Sparse Dictionary-Based Features for View-Independent Action Unit Intensity Estimation
Mohammadreza Amirian, Markus Kächele, Günther Palm, Friedhelm Schwenker · 2017
In this paper, a robust system for view-independent action unit intensity estimation is presented. Based on the theory of sparse coding, region-specific dictionaries are trained to approximate the characteristic of the individual action units. The system incorporates landmark detection, face alignment and contrast normalization to handle a large variety of different scenes. Coupled with head pose estimation, an ensemble of large margin classifiers is used to detect the individual action units. The experimental validation shows that our system is robust against pose variations and able to outperform the challenge baseline by more than 35%.