关键词: 面部表情识别, 纹理特征, 几何特征, 多核融合

Abstract: Facial expression recognition, one of the most important approaches to delivery emotion, attracts many researchers?? attention in these years as a crucial part of affective computing. As similar to classical pattern recognition tasks, the key of expression recognition is how to represent more discriminative statistical features. Meanwhile, how to combine different types of features efficiently plays a vital role in performance improvements, since various kinds of features have weighted contributions. In this paper, mark points are selected according to significant geometric changes to form geometric features, and appearance features are extracted from blocks constructed based on geometric feature. Multi-kernel trick is also employed in the classifier. Experiments on a public dataset (CK+) and a self-build dataset (JNFE), compared with classical methods and state-of-the-art methods, 96.90% and 92.85% accuracy rates are achieved respectively, proving the efficiency of proposal method.

Key words: facial expression recognition, texture features, geometric features, multi-kernel fusion