Title
Improved facial expression recognition via uni-hyperplane classification
Abstract
Large margin learning approaches, such as support vector machines (SVM), have been successfully applied to numerous classification tasks, especially for automatic facial expression recognition. The risk of such approaches however, is their sensitivity to large margin losses due to the influence from noisy training examples and outliers which is a common problem in the area of affective computing (i.e., manual coding at the frame level is tedious so coarse labels are normally assigned). In this paper, we leverage the relaxation of the parallel-hyperplanes constraint and propose the use of modified correlation filters (MCF). The MCF is similar in spirit to SVMs and correlation filters, but with the key difference of optimizing only a single hyperplane. We demonstrate the superiority of MCF over current techniques on a battery of experiments.
Year
Venue
Keywords
2012
CVPR
Improved facial expression recognition,modified correlation filter,large margin loss,automatic facial expression recognition,common problem,uni-hyperplane classification,current technique,coarse label,affective computing,frame level,large margin,correlation filter
DocType
Citations 
PageRank 
Conference
3
0.37
References 
Authors
0
1
Name
Order
Citations
PageRank
P. Lucey130.37