Abstract
Facial expression recognition in video has attracted growing attention recently. In this paper, we propose to handle this problem with dynamic appearance and geometric features. We propose a new feature descriptor called HOG from Three Orthogonal Planes (HOG-TOP) to represent dynamic features. In addition, we introduce two types of geometry features to represent the facial rigid changes and non-rigid changes, respectively. Multiple Kernel Learning (MKL) is applied to find an optimal combination of two types of features. And finally a Support Vector Machine (SVM) with multiple kernels is trained for the facial expression classification. Extensive experiments conducted on the extended Cohn-Kanade dataset show that our method can achieve a competitive performance compared with the other state-of-the-art methods.
Original language | English |
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Title of host publication | 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings |
Publisher | IEEE Computer Society |
Pages | 4967-4971 |
Number of pages | 5 |
Volume | 2015-December |
ISBN (Electronic) | 9781479983391 |
DOIs | |
Publication status | Published - 9 Dec 2015 |
Event | IEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada Duration: 27 Sep 2015 → 30 Sep 2015 |
Conference
Conference | IEEE International Conference on Image Processing, ICIP 2015 |
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Country/Territory | Canada |
City | Quebec City |
Period | 27/09/15 → 30/09/15 |
Keywords
- Facial expression recognition
- Geometry features
- Multiple Kernel Learning
- texture
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing