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Open AccessArticle

Active AU Based Patch Weighting for Facial Expression Recognition

Computer Vision Institute, School of Computer Science & Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
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Author to whom correspondence should be addressed.
Sensors 2017, 17(2), 275; https://doi.org/10.3390/s17020275
Received: 30 December 2016 / Accepted: 24 January 2017 / Published: 30 January 2017
(This article belongs to the Section Sensor Networks)
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. View Full-Text
Keywords: expression recognition; expression triplet; feature optimization; AU weighting; active AU detection expression recognition; expression triplet; feature optimization; AU weighting; active AU detection
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MDPI and ACS Style

Xie, W.; Shen, L.; Yang, M.; Lai, Z. Active AU Based Patch Weighting for Facial Expression Recognition. Sensors 2017, 17, 275. https://doi.org/10.3390/s17020275

AMA Style

Xie W, Shen L, Yang M, Lai Z. Active AU Based Patch Weighting for Facial Expression Recognition. Sensors. 2017; 17(2):275. https://doi.org/10.3390/s17020275

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. https://doi.org/10.3390/s17020275

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