结合SoftPool的VGG19与CapsNet相级联的表情识别模型研究

Research on Expression Recognition Model Cascading with VGG19 and CapsNet of SoftPool

摘要:

表情识别作为计算机视觉领域的研究热点,在情感识别、人机交互、智能安防等领域有着重要的应用。文章针对VGG19在训练人脸表情数据集时由于全连接层参数量过大而易过拟合的问题,利用胶囊网络CapsNet对VGG19的全连接层进行替换,以实现VGG19与CapsNet相级联,从而改善训练时过拟合的问题,同时使得级联后的模型在RAF-DB数据集上的精度提高了5.28%。针对VGG19特征提取网络的MaxPool易丢失人脸特征图信息的问题,利用SoftPool对MaxPool进行替换,从而在最大程度上保留了人脸的细粒度特征。实验结果表明,改进后的模型在RAF-DB数据集上取得了84.21%的精度,在FER2013数据集上取得了73.16%的精度,表情识别效果更优。

Abstract:

As a research hotspot in the field of computer vision, expression recognition plays an important role in emotion recognition, human-computer interaction, intelligent security and other fields. Aiming at the problem that VGG19 is easy to overfit due to the large number of fully connected layer parameters in the process of data training, CapsNet is used to replace the fully connected layer of VGG19 to realize the cascade of VGG19 and CapsNet, so as to improve the problem of overfitting during training. At the same time, the accuracy of the cascaded model on the RAF-DB dataset is improved by 5.28%. In view of the problem that the MaxPool of VGG19 feature extraction network is easy to lose information of face feature map, the SoftPool is used to replace MaxPool, so as to retain the fine-grain features of face to the maximum extent. The experiments show that the accuracy of the two improved models is 84.21% on the RAF-DB dataset and 73.16% on the FER2013 dataset, which has a good expression recognition effect.