Facial Expression Recognition from Video Sequences Based on Spatial-Temporal Motion Local Binary Pattern and Gabor Multiorientation Fusion Histogram

This paper proposes novel framework for facial expressions analysis using dynamic and static information in video sequences. First, based on incremental formulation, discriminative deformable face alignment method is adapted to locate facial points to correct in-plane head rotation and break up faci...

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Bibliographic Details
Published in:Mathematical problems in engineering 2017-01, Vol.2017 (2017), p.1-12
Main Authors: Zhao, Lei, Zhang, Guoxin, Wang, Zengcai
Format: Article
Language:English
Subjects:
Accuracy
Datasets
Dynamics
Face recognition
Facial
Formability
Head movement
Histograms
Laboratories
Mathematical analysis
Mechanical engineering
Methods
Pattern analysis
Pattern recognition
Principal components analysis
Sequences
Support vector machines
Texture
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Summary:This paper proposes novel framework for facial expressions analysis using dynamic and static information in video sequences. First, based on incremental formulation, discriminative deformable face alignment method is adapted to locate facial points to correct in-plane head rotation and break up facial region from background. Then, spatial-temporal motion local binary pattern (LBP) feature is extracted and integrated with Gabor multiorientation fusion histogram to give descriptors, which reflect static and dynamic texture information of facial expressions. Finally, a one-versus-one strategy based multiclass support vector machine (SVM) classifier is applied to classify facial expressions. Experiments on Cohn-Kanade (CK) + facial expression dataset illustrate that integrated framework outperforms methods using single descriptors. Compared with other state-of-the-art methods on CK+, MMI, and Oulu-CASIA VIS datasets, our proposed framework performs better.
ISSN:1024-123X
1563-5147
DOI:10.1155/2017/7206041