Multi-scale primal feature based facial expression modeling and identification

L. Yin, Xiaozhou Wei
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引用次数: 9

Abstract

In this paper, we present our newly developed face expression modeling system for expression analysis and identification. Given a face image at a front view, a realistic facial model is created using our extended topographic analysis and model instantiation approach. Our facial expression modeling system consists of two major components: (1) facial feature representation using the coarse-to-fine multiscale topographic primitive features and (2) an adaptive generic model individualization process based on the primal facial surface feature context. The algorithms have been tested using both static images and facial expression sequences. The usefulness of the generated expression models is validated by our 3D facial expression analysis algorithm. The accuracy of the generated expression model is evaluated by the comparison between the generated models and the range models obtained by a 3D digitizer
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基于多尺度原始特征的面部表情建模与识别
本文介绍了一种基于表情分析和识别的人脸表情建模系统。给定正面视图的面部图像,使用我们的扩展地形分析和模型实例化方法创建逼真的面部模型。我们的面部表情建模系统由两个主要部分组成:(1)使用粗到细的多尺度地形原始特征的面部特征表示;(2)基于原始面部特征上下文的自适应通用模型个性化过程。这些算法已经在静态图像和面部表情序列上进行了测试。通过我们的三维面部表情分析算法验证了生成的表情模型的有效性。通过将生成的表达式模型与三维数字化仪获得的距离模型进行比较,对生成的表达式模型的精度进行评价
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