Session 4B: Friday Afternoon, December 6th Poster Spotlights Session 4B: Friday Afternoon, December 6th Capturing Global Semantic Relationships for Facial Action Unit Recognition Ziheng Wang, Yongqiang Li, Shangfei Wang, Qiang Ji

P4B-09 Motivation Approach Result Capturing Global Semantic Relationships for Facial Action Unit Recognition Motivation Facial action units (AU) are NOT independent Current methods can only model pair-wise AU relations Propose to use RBM to capture global AU relations for AU recognition Happiness Surprise Anger Fear Stretch mouth Raise eye lid Raise brow Raise cheek Pull lip corner Approach Result CK+ SEMAINE In this paper we tackle the problem of facial action unit recognition by exploiting the complex semantic relationships among the action units. The relationships among AUs contain crucial information yet have not been thoroughly exploited. We build a hierarchical model that combines the bottom-level image features and the top-level AU relationships to jointly recognize AUs in a principled manner. The proposed model has two major advantages over existing methods. 1) Unlike methods that can only capture local pair-wise AU dependencies, our model is developed upon the restricted Boltzmann machine and therefore can exploit the global relationships among AUs. 2) Although AU relationships are influenced by many related factors such as facial expressions, these factors are generally ignored by the current methods. Our model, however, can successfully capture them to more accurately characterize the AU relationships. Experimental results on benchmark databases demonstrate the effectiveness of the proposed approach in modelling complex AU relationships as well as its superior AU recognition performance over existing approaches.   Top Down: Global semantic relations among facial action units Bottom Up: AU recognition from image measurements