26 February 2010 Feature selection for facial expression recognition using deformation modeling
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Proceedings Volume 7546, Second International Conference on Digital Image Processing; 75462A (2010); doi: 10.1117/12.853220
Event: Second International Conference on Digital Image Processing, 2010, Singapore, Singapore
Abstract
Works on Facial Expression Recognition (FER) have mostly been done using image based approaches. However, in recent years, researchers have also been trying to explore the use of 3D information for the task of FER. Most of the time, there is a need for having a neutral (expressionless) face of the subject in both the image based and 3D model based approaches. However, this might not be practical in many applications. This paper tries to address this limitations in previous works by proposing a novel technique of feature extraction which does not require any neutral face of the subjects. It has been proposed and validated experimentally that the motion of some landmark points on the face, in exhibiting a particular facial expression, is similar in different persons. Separate classifier is made and relevant feature points are selected for each expression. One vs all SVM classification gives promising results.
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Ruchir Srivastava, Terence Sim, Shuicheng Yan, Surendra Ranganath, "Feature selection for facial expression recognition using deformation modeling", Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75462A (26 February 2010); doi: 10.1117/12.853220; https://doi.org/10.1117/12.853220
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KEYWORDS
3D modeling

Facial recognition systems

Feature extraction

Motion models

Feature selection

3D image processing

Data modeling

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