3D Facial Expression Classification Based on Self-Organizing Mapping Network

Xiaojuan Yin, Quan Ju, Shuhong Li
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Abstract

In this paper, we propose and explore a novel method to recognize human facial expression in 3D. Based on a shape descriptor to express shape in 3D converted from the original 3D points cloud, we localize the different features in 3D shape around the nose tip. Then we exploit Self-Organizing Map(SOM) to recognize the similarity of the same expression and the variations between different expresses. Experiments performed on data with facial expression variations show that our method is able to separate different expressions.
基于自组织映射网络的三维面部表情分类
本文提出并探索了一种新的三维人脸表情识别方法。基于由原始三维点云转换而来的三维形状描述符,对鼻尖周围三维形状的不同特征进行了定位。然后利用自组织映射(SOM)识别相同表达的相似性和不同表达之间的差异。对面部表情变化数据进行的实验表明,我们的方法能够区分不同的表情。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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