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.
Published Version
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