Recognition of 3D facial expression dynamics
Recognition of 3D facial expression dynamics
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DOI：
10.1016/j.imavis.2012.01.006
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发表时间：
2012-10-01
影响因子：
4.7
通讯作者：
Rueckert, Daniel
中科院分区：
文献类型：
--
作者：
Sandbach, Georgia;Zafeiriou, Stefanos;Rueckert, Daniel
在本文中，我们提出了一种方法，利用三维人脸几何序列的帧之间的三维运动为基础的功能，用于动态面部表情识别。一个表达序列被建模为包含一个开始，然后是一个顶点和一个偏移。应用特征选择方法，以便为表达式的开始和偏移段中的每一个提取特征。然后，这些特征用于训练GentleBoost分类器并构建隐马尔可夫模型，以便对表达的完整时间动态进行建模。建议的全自动系统被用于BU-4DFE数据库，用于区分六种通用表达：快乐，悲伤，愤怒，厌恶，惊讶和恐惧。还进行了与基于从面部强度图像中提取的运动的类似2D系统的比较。所获得的结果表明，使用的3D信息确实提高了识别精度相比，2D数据在一个完全自动的方式。(c)2012爱思唯尔有限公司版权所有。
In this paper we propose a method that exploits 3D motion-based features between frames of 3D facial geometry sequences for dynamic facial expression recognition. An expressive sequence is modelled to contain an onset followed by an apex and an offset. Feature selection methods are applied in order to extract features for each of the onset and offset segments of the expression. These features are then used to train GentleBoost classifiers and build a Hidden Markov Model in order to model the full temporal dynamics of the expression. The proposed fully automatic system was employed on the BU-4DFE database for distinguishing between the six universal expressions: Happy, Sad, Angry, Disgust, Surprise and Fear. Comparisons with a similar 2D system based on the motion extracted from facial intensity images was also performed. The attained results suggest that the use of the 3D information does indeed improve the recognition accuracy when compared to the 2D data in a fully automatic manner. (c) 2012 Elsevier B.V. All rights reserved.