1. 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

2. 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