Active AU Based Patch Weighting for Facial Expression Recognition
AbstractFacial 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. View Full-Text
Share & Cite This Article
Xie, W.; Shen, L.; Yang, M.; Lai, Z. Active AU Based Patch Weighting for Facial Expression Recognition. Sensors 2017, 17, 275.
Xie W, Shen L, Yang M, Lai Z. Active AU Based Patch Weighting for Facial Expression Recognition. Sensors. 2017; 17(2):275.Chicago/Turabian Style
Xie, Weicheng; Shen, Linlin; Yang, Meng; Lai, Zhihui. 2017. "Active AU Based Patch Weighting for Facial Expression Recognition." Sensors 17, no. 2: 275.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.