Learning spatial weighting for facial expression analysis via constrained quadratic programming
作者：
Highlights：
• A novel optimization formulation for facial expression analysis is proposed.
• This formulation learns spatial weighting for optical flow due to facial expression.
• This learning formulation leads to solving a quadratic programming problem.
• Facial landmarks are first located, and optical flow is computed as the motion features.
• Accuracy is greatly improved on expression recognition and intensity estimation experiments.
摘要
•A novel optimization formulation for facial expression analysis is proposed.•This formulation learns spatial weighting for optical flow due to facial expression.•This learning formulation leads to solving a quadratic programming problem.•Facial landmarks are first located, and optical flow is computed as the motion features.•Accuracy is greatly improved on expression recognition and intensity estimation experiments.
论文关键词：Facial expression analysis,Quadratic programming,Expression recognition,Expression intensity estimation
论文评审过程：Received 19 June 2012, Revised 11 February 2013, Accepted 25 March 2013, Available online 6 April 2013.
论文官网地址：https://doi.org/10.1016/j.patcog.2013.03.017
