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Posed and spontaneous expression recognition through modeling their spatial patterns

机译：通过建模其空间模式来进行正确和自发的表情识别

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摘要

This paper presents a new method to recognize posed and spontaneous expressions through modeling their spatial patterns. Gender and expression categories are employed as privileged information to further improve the recognition. The proposed approach includes three steps. First, geometric features about facial shape and Action Unit variations are extracted from the differences between apex and onset facial images to capture the spatial facial variation. Second, statistical hypothesis testings are conducted to explore the differences between posed and spontaneous expressions using the defined geometric features from three aspects: all samples, samples given the gender information, and samples given expression categories. Third, several Bayesian networks are built to capture posed and spontaneous spatial facial patterns respectively given gender and expression categories. The statistical analysis results on the USTC-NVIE and SPOS databases both demonstrate the effectiveness of the proposed geometric features. The recognition results on the USTC-NVIE database indicate that the privileged information of gender and expression can help model the spatial patterns caused by posed and spontaneous expressions. The recognition results on both databases outperform those of the state of the art.
机译：本文提出了一种通过对姿势和自发表情进行空间建模来识别它们的新方法。性别和表达类别被用作特权信息，以进一步提高识别度。提议的方法包括三个步骤。首先，从顶点和面部面部图像之间的差异中提取有关面部形状和动作单元变化的几何特征，以捕获空间面部变化。第二，进行统计假设检验，从三个方面使用定义的几何特征探索姿势和自发表达之间的差异：所有样本，具有性别信息的样本以及具有表达类别的样本。第三，建立了多个贝叶斯网络以分别在给定性别和表情类别的情况下捕获姿势和自发的空间面部模式。在USTC-NVIE和SPOS数据库上的统计分析结果都证明了所提出的几何特征的有效性。在USTC-NVIE数据库上的识别结果表明，性别和表达的特权信息可以帮助对由姿势和自发表达引起的空间模式进行建模。两个数据库上的识别结果均优于现有技术。

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  • 来源
    《Machine Vision and Applications》 |2015年第3期|219-231|共13页
  • 作者

    Shangfei Wang; Chongliang Wu; Menghua He; Jun Wang; Qiang Ji;

  • 作者单位

    Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, People's Republic of China;

    Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, People's Republic of China;

    Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, People's Republic of China;

    Key Lab of Computing and Communication Software of Anhui Province, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, People's Republic of China;

    Department of Electrical, Computer, and Systems Engineering,Rensselaer Polytechnic Institute, Troy, NY 12180, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Posed and spontaneous expression recognition; Spatial pattern; Priviledged information; Gender; Expression categories;

    机译：正确且自发的表情识别;空间格局;特权信息;性别;表达类别;

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