Paper Info
Title
Estimation of facial action intensities on 2D and 3D data
Abstract
The paradigm of Facial Action Coding System (FACS) offers a comprehensive solution for facial expression measurements. FACS defines atomic expression components called Action Units (AUs) and describes their strength on a five-point scale. Despite considerable progress in AU detection, the AU intensity estimation has not been much investigated. We propose SVM-based regression on AU feature space, and investigate person-independent estimation of 25 AUs that appear singly or in various combinations. Our method is novel in that we use regression for estimating intensities and comparatively evaluate the performances of 2D and 3D modalities. The proposed technique shows improvements over the state-of-the-art person-independent estimation, and that especially the 3D modality offers significant advantages for intensity coding. We have also found that fusion of 2D and 3D can boost the estimation performance, especially when modalities compensate for each other's shortcomings.
Year
Venue
Keywords
2011
Barcelona
emotion recognition,face recognition,image coding,object detection,regression analysis,support vector machines,2d data,3d data,au detection,au feature space,facs,svm-based regression,action units,atomic expression components,facial action coding system,facial action intensity estimation,facial expression measurements,intensity coding,person-independent estimation,correlation,feature extraction,face,estimation,gold
Field
DocType
ISSN
Feature vector,Facial Action Coding System,Pattern recognition,Regression,Support vector machine,Coding (social sciences),Feature extraction,Facial expression,Correlation,Artificial intelligence,Mathematics
Conference
2076-1465
Citations 
PageRank 
References 
3
0.44
10
Authors
3
Name
Order
Citations
PageRank
Arman Savran155318.63
Bulent Sankur227821.93
Taha Bilge, M.330.44