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Facial-expression recognition based on a low-dimensional temporal feature space
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AI
DOI：10.1007/s11042-017-5354-x.png)
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Abstract
En 中文
This paper suggests a facial-expression recognition in accordance with face video sequences based on a newly low-dimensional feature space proposed. Indeed, we extract a Pyramid of uniform Temporal Local Binary Pattern representation, using only XT and YT orthogonal planes (PTLBP (u2)). Then, a Wrapper method is applied to select the most discriminating sub-regions, and therefore, reduce the feature space that is going to be projected on a low-dimensional feature space by applying the Principal Component Analysis (PCA). Support Vector Machine (SVM) and C4.5 algorithm have been tested for the classification of facial expressions. Experiments conducted on CK + and MMI, which are the two famous facial-expression databases, have shown the effectiveness of the approach proposed under a lab-controlled environment with more than 97% of recognition rate as well as under an uncontrolled environment with more than 92%.
Keywords:
Facial-expression recognition
Pyramid of uniform Temporal Local Binary Pattern (PTLBPu2)
Principal Component Analysis (PCA)
Discriminating sub-regions
Low-dimensional feature space
Journal
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3
Papers:
1.9W
Citations:
3.2W