作者:苗壮 ，程卫月 ，林克正 ，李骜
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Authors:MIAO Zhuang ,CHENG Wei-yue ,LIN Ke-zheng ,LI Ao
摘要:摘要:针对单一卷积神经网络对人脸表情特征提取不充分和参数量较大等问题，提出了一种融合并行网络特征的人脸表情识别算法。该算法首先对ResNet网络中的残差块进行修改，减少网络参数量同时使用预激活来减小错误率。之后将改进后的ResNet网络提取到的特征与剪层后的VGG网络提取到的特征进行融合，得到网络模型P-ResNet-VGG，其中损失函数使用交叉熵损失函数。该模型已在FER2013和JAFFE数据集上进行了大量实验。实验结果表明，该模型比其他几种模型在FER2013和JAFFE表情数据集上准确率都有所提高，具有更好的鲁棒性。
Abstract:Abstract:Aiming at the problems of insufficient extraction of facial expression features by a single convolutional neural network and large amount of parameters, a facial expression recognition algorithm fused with parallel network features is proposed. The algorithm first modifies the residual block in the ResNet network, reduces the amount of network parameters and uses pre-activation to reduce the error rate. After that, the features extracted by the improved ResNet network and the features extracted by the VGG network after the cut layer are merged to obtain the network model P-ResNet-VGG, in which the loss function uses the cross-entropy loss function. This model has been extensively tested on the FER2013 and JAFFE datasets. Experimental results show that this model has improved accuracy on FER2013 and JAFFE expression data sets than other models, and has better robustness.

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