Facial Expression Recognition Based on Feature Point Vector and Texture Deformation Energy Parameters
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摘要: 人脸表情识别是人机交互领域的研究热点和难点之一。为了有效去除由于个体差异而造成的表情特征的差异,该文首先基于特征点矢量提出特征点距离比例系数;其后,又给出纹理形变能量参数的概念;最后,将二者融合用于人脸表情识别。所提方法在Cohn-Kanade数据库及BHU人脸表情数据库进行了测试,实验结果表明该方法较传统的方法在识别率上分别提高了4.5%与3.9%。Abstract: Facial expression recognition is a popular and difficult research field in human-computer interaction. In order to remove effectively the differences in expression feature caused by individual differences, this paper firstly presents the feature point distance ratio coefficient based on feature point vector, and then gives the concept of texture deformation energy parameters. Finally, merges previously mentioned two parts to form a new expression feature for facial expression recognition. The proposed method is tested in the Cohn-Kanade database and the BHU facial expression database, and the experimental results show the recognition rates of the proposed method comparing with the existing ones increased by 4.5% and 3.9%.
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