摘要
基于对人类表情肌活动效果的归纳,采用一种新的面部特征构造描述面部状态。以支持向量机的后验概率作为依据,提出一种基于面部肌肉特征的面部表情度量方法,并对基于不同特征和不同面部素材库的决策模型进行对比实验。结果表明,相比其他的方法,基于新特征的度量方法能够对不同的面部表情产生具有足够区分度的度量,并能够以较高的准确率提取视频文件中"最夸张"的表情。
A new facial feature produced from a set of primitive feature new feature are proposed. Development of the new feature is based points and a facial expression scoring method based on the on the summarization of human facial muscle movement effects. And the scoring method uses posteriori produced by SVM as the foundation of the scoring results. In the experiment, be- sides facial muscle feature, positions of the primitive feature points as a whole feature vector are added as a comparison, as well as different decision-making models and different sources of testing set. Based on the new feature the scoring system provides e- nough distinguishing scoring results on expressions of different intensities, and extracts ＇the most intensive＇ expression frame from videos with a rather high accuracy.
出处
《中国科技论文》
CAS
北大核心
2013年第10期1011-1016,共6页
China Sciencepaper
基金
国家自然科学基金资助项目(61003205)
关键词
计算机应用技术
面部表情度量
面部肌肉特征
支持向量机
technology of computer application
facial expression measurement
facial muscle features
support vector machine
