An optimal transportation-based recognition algorithm for 3D facial expressions

IF 0.4 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Tie-xiang Li, Pei Chuang, M. Yueh
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引用次数: 0

Abstract

Facial expression recognition (FER) is an active topic that has many applications. The development of effective algorithms for FER has been a competitive research field in the last two decades. In this paper, we propose a fully automatic 3D FER method based on the sparse approximation of 2D feature images. For a prescribed feature defined on the 3D facial surface, we apply a parameterization that not only maps the facial surface onto the unit disk but also locally preserves the feature. To ensure the uniqueness of the solution, some aligning constraints are further taken into account while computing the desired parameterization. The facial surface associated with the feature is then converted into the 2D image of the parameter domain. To recognize the expression of a test facial image, we apply an existing 2D expression recognition model, which is built upon sparse representation. Numerical experiments indicate that the accuracy of the proposed FER algorithm reaches 71.42% on a benchmark facial expression database, which is promising for practical applications.
一种基于交通的三维面部表情识别算法
面部表情识别(FER)是一个应用广泛的活跃课题。在过去的二十年中,开发有效的FER算法一直是一个竞争激烈的研究领域。本文提出了一种基于二维特征图像稀疏逼近的全自动三维FER方法。对于定义在三维曲面上的指定特征,我们采用参数化方法,不仅将曲面映射到单元磁盘上,而且局部保留该特征。为了保证解的唯一性,在计算所需参数化时进一步考虑了一些对齐约束。然后将与特征相关联的面部表面转换为参数域的二维图像。为了识别测试面部图像的表情,我们应用了现有的基于稀疏表示的二维表情识别模型。数值实验表明,该算法在一个基准面部表情数据库上的准确率达到71.42%,具有较好的应用前景。
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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来源期刊
Annals of Mathematical Sciences and Applications
Annals of Mathematical Sciences and Applications MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
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