计算机工程 ›› 2019, Vol. 45 ›› Issue (1): 186-191.doi: 10.19678/j.issn.1000-3428.0049148

• 人工智能及识别技术 • 上一篇    下一篇

基于区域与分类回归融合的AU强度估计算法

陈欣键,姜飞,申瑞民,胡巧平   

  1. 上海交通大学 计算机科学与技术系,上海 200240
  • 收稿日期:2017-11-01 出版日期:2019-01-15 发布日期:2019-01-15
  • 作者简介:陈欣键(1993—),男,硕士,主研方向为人脸情感计算;姜飞,博士;申瑞民,教授;胡巧平,博士
  • 基金项目:

    国家自然科学基金面上项目(61671290);国家重点研发计划(2016YFE0129500);上海市科委项目(17511101903)

AU Strength Estimation Algorithm Based on Region and Classification Regression Fusion

CHEN Xinjian,JIANG Fei,SHEN Ruimin,HU Qiaoping   

  1. Department of Computer Science and Engineering,Shanghai Jiaotong University,Shanghai 200240,China
  • Received:2017-11-01 Online:2019-01-15 Published:2019-01-15

摘要:

脸部动作编码系统为人脸表情信息定义了脸部动作单元(AU)的概念,但在AU强度的检测上由于各级别之间的区分度较低且个体间人脸表情差异较大,导致检测效果较差。为此,挖掘AU激活和区域之间较强的相关特性,提出一种新的基于区域和特征融合的特征提取算法,并同时给出一种AU强度计算方法,即在对高AU强度和低AU强度二分类后根据有序回归判断AU最终的强度。该算法利用强AU和弱AU较强的可分性,考虑不同AU强度间的相关性,发挥分类和回归方法在AU强度检测方面的优势。在DISFA、FERA2015数据集上的实验结果表明,该算法具有较高的鲁棒性,AU强度的计算效果优于CNN、VGG16等方法。

关键词: 运动单元, 强度检测, 区域特征提取, 特征融合, 支持向量机, 有序回归

Abstract:

The facial action coding system defines the concept of facial Action Unit (AU) for facial expression information,but the detection effect of AU strength is poor due to the low degree of discrimination between the levels and the large difference in facial expression between individuals.To this end,mining the strong correlation between AU activation and region,a new feature extraction algorithm based on region and feature fusion is proposed,and an AU strength calculation method is given,high AU strength and low AD strength are classified,the final strength of the AU is judged based on the ordered regression.The algorithm utilizes the strong separability of strong AU and weak AU,considers the correlation between different AU strengtis,and exerts the advantages of classification and regression methods in AU strength detection.Experimental results on the DISFA and FERA2015 datasets show that the proposed algorithm is robust and the AU strengh is better than CNN and VGG16.

Key words: Action Unit(AU), strength detection, region feature extraction, feature fusion, Support Vector Machine(SVM), ordered regression

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