Paper Info
Title
Towards the Usage of Optical Flow Temporal Features for Facial Expression Classification.
Abstract
Psychological evidence suggests that the human ability to recognize facial expression improves with the addition of temporal stimuli. While the facial action coding community has largely migrated towards temporal information, the facial expression recognition community has been slow to utilize facial dynamics. This paper contrasts the contributions of static vs. temporal features, including both dense and sparse facial tracking methodologies in combination with sparse representation classification. The temporal methods of facial feature point tracking, motion history images, free form deformation, and SIFT flow are adapted for facial expression classification. Dense optical flow for facial expression recognition is successfully utilized. We show that when used in isolation, the best temporal methods are just as good as static methods. However, when fusing temporal dynamics with static imagery significant increases in facial expression classification are achieved.
Year
DOI
Venue
2012
10.1007/978-3-642-33191-6_38
ADVANCES IN VISUAL COMPUTING, ISVC 2012, PT II
Field
DocType
Volume
Scale-invariant feature transform,Computer vision,Pattern recognition,Computer science,Local binary patterns,Sparse approximation,Free-form deformation,Facial expression,Artificial intelligence,Motion History Images,Optical flow,Motion vector
Conference
7432
ISSN
Citations 
PageRank 
0302-9743
2
0.42
References 
Authors
19
2
Name
Order
Citations
PageRank
Raymond W. Ptucha111322.42
Andreas Savakis237741.10