Active AU Based Patch Weighting for Facial Expression Recognition

作者：Xie, Weicheng; Shen, Linlin^*; Yang, Meng; Lai, Zhihui

来源：Sensors (Switzerland), 2017, 17(2): 275.

DOI：10.3390/s17020275

• expression recognition
• expression triplet
• feature optimization
• AU weighting
• active AU detection

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.

• 出版日期2017-2
• 单位深圳大学