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Subject Independent Facial Expression Recognition from 3D Face Models using Deformation Modeling
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Author(s): Ruchir Srivastava (National University of Singapore, Singapore), Shuicheng Yan (National University of Singapore, Singapore), Terence Sim (National University of Singapore, Singapore)and Surendra Ranganath (Indian Institute of Technology, Gandhinagar, India)
Copyright: 2012
Pages: 22
Source title:
Depth Map and 3D Imaging Applications: Algorithms and Technologies
Source Author(s)/Editor(s): Aamir Saeed Malik (Universiti Teknologi Petronas, Malaysia), Tae Sun Choi (Gwangju Institute of Science and Technology, Korea)and Humaira Nisar (Universiti Tunku Abdul Rahman, Malaysia)
DOI: 10.4018/978-1-61350-326-3.ch030
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Abstract
Most of the works on Facial Expression Recognition (FER) have worked on 2D images or videos. However, researchers are now increasingly utilizing 3D information for FER. As a contribution, this chapter zooms in on 3D based approaches while introducing FER. Prominent works are reviewed briefly, and some of the issues involved in 3D FER are discussed along with the future research directions. In most of the FER approaches, there is a need for having a neutral (expressionless) face of the subject which might not always be practical. This chapter also presents a novel technique of feature extraction which does not require any neutral face of the test subject. A proposition has been verified experimentally that motion of a set of landmark points on the face, in exhibiting a particular facial expression, is similar in different persons. The presented approach shows promising results using Support Vector Machine (SVM) as the classifier.
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