机构地区: 北京大学信息科学技术学院视觉听觉信息处理国家重点实验室
出 处: 《计算机辅助设计与图形学学报》 2005年第11期2394-2401,共8页
摘 要: 给出一种基于特征加权聚类的表情识别算法.首先通过特征分组加权充分考虑特征之间度量值的不均衡性,更好地描述了同类表情中不同特征作用的差异;其次利用模糊聚类思想在算法中引入表情不确定性描述,给出了基于形状特征识别表情时表情的模糊表示方法.该算法实现简单,计算复杂度低,能够实时、动态地更新训练结果,并且有良好的分类效果. In this paper, we present a method of facial expression recognition based on weighted clustering of grouped features. At first, we design a feature-grouping method for normalizing feature values to account for different effects of the features for the expression classification. Secondly, a fuzzy clustering algorithm by weighting the grouped features is introduced to tackle the expression uncertainty. The method is computationally simple, easy to implement, and can be used for dynamic updating for an on-line training and recognition system.
领 域: [自动化与计算机技术] [自动化与计算机技术]