Affective computing: challenges
International Journal of Human-Computer Studies - Application of affective computing in humanComputer interaction
Active and Dynamic Information Fusion for Facial Expression Understanding from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bosphorus Database for 3D Face Analysis
Biometrics and Identity Management
Automatic coding of facial expressions displayed during posed and genuine pain
Image and Vision Computing
A Unified Probabilistic Framework for Spontaneous Facial Action Modeling and Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Dynamic Texture-Based Approach to Recognition of Facial Actions and Their Temporal Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of facial expressions and measurement of levels of interest from video
IEEE Transactions on Multimedia
Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility
Hi-index | 0.00 |
This paper proposed a probabilistic approach to divide the Facial Action Units (AUs) based on the physiological relations and their strengths among the facial muscle groups. The physiological relations and their strengths were captured using a Static Bayesian Network (SBN) from given databases. A data driven spatio-temporal probabilistic scoring function was introduced to divide the AUs into : (i) frequently occurred and strongly connected AUs (FSAUs) and (ii) infrequently occurred and weakly connected AUs (IWAUs). In addition, a Dynamic Bayesian Network (DBN) based predictive mechanism was implemented to predict the IWAUs from FSAUs. The combined spatio-temporal modeling enabled a framework to predict a full set of AUs in real-time. Empirical analyses were performed to illustrate the efficacy and utility of the proposed approach. Four different datasets of varying degrees of complexity and diversity were used for performance validation and perturbation analysis. Empirical results suggest that the IWAUs can be robustly predicted from the FSAUs in real-time and was found to be robust against noise.