Article
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Active AU Based Patch Weighting for Facial Expression Recognition
Version 1
: Received: 26 January 2017 / Approved: 26 January 2017 / Online: 26 January 2017 (08:52:52 CET)
A peer-reviewed article of this Preprint also exists.
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, 275.
Journal reference: Sensors 2017, 17, 275
DOI: 10.3390/s17020275
Abstract
Facial expression has lots of 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. Sparse representation based approach is then proposed to detect the active AUs of testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for Jaffe and CK+ databases, respectively. Better cross-database performance has also been observed.
Subject Areas
expression recognition; expression triplet; feature optimization; AU weighting; active AU detection.
Copyright: 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.
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