1. Session 3D: Thursday Afternoon, June 27th
Capturing Complex Spatio-Temporal Relations among Facial Muscles for Facial Expression Recognition
Ziheng Wang, Shangfei Wang, Qiang Ji

2. Interval Temporal Bayesian Network
P3D-06
Capturing Complex Spatio-Temporal Relations among Facial Muscles for Facial Expression Recognition
Introduction
Model the facial expression as a complex activity consisting of sequential or overlapping facial muscle events
Propose an Interval Temporal Bayesian Network (ITBN) to capture spatio-temporal relations among the primitive facial events for expression recognition
Interval Temporal Bayesian Network B C A
before
meet
overlap
start
during
finish
equal
IAB
IAC
IBC t E1 E2 E3 E4
Experimental Results
ITBN outperforms other time-slice based dynamic models such as HMM
ITBN achieves comparable and even better performance than the related works
ITBN
HMM
Lucey et al.
CK+
86.3%
83.5%
83.3%
Zhong et al.
MMI
59.7%
51.5%
49.4%
Contributions
Standard Dynamic Models
Proposed Model
Sequential events
Sequential or overlapping events
Local stationary relations
Global relations
3 relations (precede, follow, equal)
13 complex relations