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. Copyright © 2015 IEEE.
|Title of host publication||2015 IEEE International Conference on Image Processing (ICIP 2015)|
|Place of Publication||Piscataway, NJ|
|ISBN (Electronic)||9781479983391, 9781479983384, 147998339X|
|Publication status||Published - 2015|
CitationChen, J., Chen, Z., Chi, Z., & Fu, H. (2015). Dynamic texture and geometry features for facial expression recognition in video. In 2015 IEEE International Conference on Image Processing (ICIP 2015) (pp. 4967-4971). Piscataway, NJ: IEEE.
- Geometry features
- Multiple Kernel Learning
- Facial expression recognition