电子学报 ›› 2016, Vol. 44 ›› Issue (6): 1307-1313.DOI: 10.3969/j.issn.0372-2112.2016.06.007

• 学术论文 • 上一篇    下一篇

结合运动时序性的人脸表情识别方法

邱玉, 赵杰煜, 汪燕芳   

  1. 宁波大学信息科学与工程学院, 浙江宁波 315211
  • 收稿日期:2014-08-08 修回日期:2015-05-17 出版日期:2016-06-25 发布日期:2016-06-25
  • 作者简介:邱玉 女,1990年12月生于山东济宁,硕士研究生.研究方向:图像处理、模式识别.E-mail:qiuyu1204@126.com;赵杰煜 男,1965年11月生于浙江宁波,教授,博士生导师.研究方向:计算智能、模式识别、人机自然交互.E-mail:zhao-jieyu@nbu.edu.cn;汪燕芳 女,1989年1月生于安徽安庆,硕士研究生.研究方向:软件开发.
  • 基金资助:

    国家自然科学基金(No.61571247);科技部国际科技合作专项(No.2013DFG12810,No.2012BAF12B11);浙江省国际科技合作专项(No.2013C24027);浙江省自然科学基金(No.LZ16F030001)

Facial Expression Recognition Using Temporal Relations Among Facial Movements

QIU Yu, ZHAO Jie-yu, WANG Yan-fang   

  1. Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang 315211, China
  • Received:2014-08-08 Revised:2015-05-17 Online:2016-06-25 Published:2016-06-25

摘要:

脸部肌肉之间的时空关系在人脸表情识别中起着重要作用,而当前的模型无法高效地捕获人脸的复杂全局时空关系使其未被广泛应用.为了解决上述问题,本文提出一种基于区间代数贝叶斯网络的人脸表情建模方法,该方法不仅能够捕获脸部的空间关系,也能捕获脸部的复杂时序关系,从而能够更加有效地对人脸表情进行识别.且该方法仅利用基于跟踪的特征且不需要手动标记峰值帧,可提高训练与识别的速度.在标准数据库CK+和MMI上进行实验发现本文方法在识别人脸表情过程中有效提高了准确率.

关键词: 表情识别, 脸部肌肉运动的时序性, 贝叶斯网络, 区间代数

Abstract:

Spatial and temporal relations between different facial muscles are very important in the facial expression recognition process.However, these implicit relations have not been widely used due to the limitation of the current models.In order to make full use of spatial and temporal information, we model the facial expression as a complex activity consisting of different facial events.Furthermore, we introduce a special Bayesian network to capture the temporal relations among facial events and develop the corresponding algorithm for facial expression modeling and recognition.We only use the features based on tracking results and this method does not require the peak frames, which can improve the speed of training and recognition.Experimental results on the benchmark databases CK+ and MMI show that the proposed method is feasible in facial expression recognition and considerably improves the recognition accuracy.

Key words: facial expression recognition, sequential facial events, Bayesian network, interval algebra

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