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Sensors 2017, 17(2), 275; doi:10.3390/s17020275

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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Received: 30 December 2016 / Accepted: 24 January 2017 / Published: 30 January 2017
(This article belongs to the Section Sensor Networks)
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Abstract

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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xie, W.; Shen, L.; Yang, M.; Lai, Z. Active AU Based Patch Weighting for Facial Expression Recognition. Sensors 2017, 17, 275.

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