Learning Expressionlets on Spatio-temporal Manifold for Dynamic Facial Expression Recognition

2014 ◽  
Author(s):  
Mengyi Liu ◽  
Shiguang Shan ◽  
Ruiping Wang ◽  
Xilin Chen
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Spatio Temporal ◽  
Dynamic Facial Expression

scholarly journals Facial Expression Recognition Based on Fused Spatio-Temporal Features

2013 ◽  
Vol 347-350 ◽  
pp. 3780-3785
Author(s):  
Jing Jie Yan ◽  
Ming Han Xin
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Feature Vector ◽  
Positional Information ◽  
Expression Recognition ◽  
Linear Filters ◽  
Temporal Features ◽  
Novel Method ◽  
Spatio Temporal ◽  
And Behavior

Although spatio-temporal features (ST) have recently been developed and shown to be available for facial expression recognition and behavior recognition in videos, it utilizes the method of directly flattening the cuboid into a vector as a feature vector for recognition which causes the obtained vector is likely to potentially sensitive to small cuboid perturbations or noises. To overcome the drawback of spatio-temporal features, we propose a novel method called fused spatio-temporal features (FST) method utilizing the separable linear filters to detect interesting points and fusing two cuboids representation methods including local histogrammed gradient descriptor and flattening the cuboid into a vector for cuboids descriptor. The proposed FST method may robustness to small cuboid perturbations or noises and also preserve both spatial and temporal positional information. The experimental results on two video-based facial expression databases demonstrate the effectiveness of the proposed method.

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