1. School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China 2. Key Lab of Computing and Communicating Software of Anhui Province, Hefei 230027, China 3. Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy NY12180-3590, USA
Most present research into facial expression recognition focuses on the visible spectrum, which is sensitive to illumination change. In this paper, we focus on integrating thermal infrared data with visible spectrum images for spontaneous facial expression recognition. First, the active appearance model AAM parameters and three defined head motion features are extracted from visible spectrum images, and several thermal statistical features are extracted from infrared (IR) images. Second, feature selection is performed using the F-test statistic. Third, Bayesian networks BNs and support vector machines SVMs are proposed for both decision-level and feature-level fusion. Experiments on the natural visible and infrared facial expression (NVIE) spontaneous database show the effectiveness of the proposed methods, and demonstrate thermal IR images’ supplementary role for visible facial expression recognition.
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