基于图卷积多标签学习的复合人脸表情识别
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GCN in Multi-label Learning for Compound Facial Expression Recognition
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    摘要:

    传统的人脸表情识别方法主要针对六类基本人脸表情,但在现实场景下,存在更加丰富的由基本人脸表情组合而成的复合人脸表情,原先识别基本人脸表情的工作难以去识别复合人脸表情,并且复合人脸表情的数据集缺乏足够的训练数据.针对该问题,提出基于图卷积多标签学习的复合人脸表情识别方法.通过特征提取网络提取到人脸表情的全局特征和感兴趣区域的局部特征,使用基本和复合人脸表情之间的先验知识和数据驱动方式,构建出表情类别关系图,利用图卷积网络来学习到表情类别分类器,最后进行复合人脸表情识别.在RAF-DB和EmotioNet数据集上的实验结果表明,与VGG19和ResNet50等方法相比,该方法可以使得复合人脸表情识别率取得约4%~5%的提升.

    Abstract:

    Traditional facial expression recognition (FER) methods have focused on the six basic facial expressions. However, compound facial expressions are also used by humans in the real world. Compound facial expression means that it is a combination of the basic facial expressions. However, the traditional methods of recognizing basic facial expressions are unable to handle compound facial expressions. Moreover, the compound facial expression datasets have insufficient training data. To address the difficulties in the compound FER, this study proposed a graph convolutional network in multi-label learning for compound facial expression recognition (GCN-ML-CFER). The global features of facial expression and the local features of the regions of interest were extracted by the feature extraction network. According to the prior knowledge of basic and compound facial expressions, a relationship graph of facial expression categories was constructed by a data-driven method. The expression category classifiers learn the graph via a graph convolutional network (GCN). Finally, compound FER was carried out by the classifiers. Experiments were conducted on the RAF-DB and EmotioNet datasets. The results show that this method achieves a 4%–5% increase in the compound FER accuracy compared with those of the VGG19 and ResNet50 methods.

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武中华.基于图卷积多标签学习的复合人脸表情识别.计算机系统应用,2022,31(1):259-266

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  • 收稿日期:2021-03-30
  • 最后修改日期:2021-04-29
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  • 在线发布日期: 2021-12-17
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