Active AU Based Patch Weighting for Facial Expression Recognition.
Facial expression has many applications in human-computer interaction. Although feature extraction and selection have been well studied, the speciﬁcity of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on action unit (AU) weighting and patch weight optimization is proposed to represent the speciﬁcity of the expression triplet. The sparse representation-based approach is then proposed to detect the active AUs of the testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for the Jaffe and Cohn-Kanade (CK+) databases, respectively. Better cross-database performance has also been observed.
Computer Vision Institute, School of Computer Science & Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China. wcxie@szu.edu.cn.
Weicheng
W
Xie
Computer Vision Institute, School of Computer Science & Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China. llshen@szu.edu.cn.
Linlin
L
Shen
Computer Vision Institute, School of Computer Science & Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China. yang.meng@szu.edu.cn.
Meng
M
Yang
Computer Vision Institute, School of Computer Science & Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China. laizhihui@szu.edu.cn.
Zhihui
Z
Lai
AU weighting; active AU detection; expression recognition; expression triplet; feature optimization