如何提取多尺度特征和建模远程通道间的语义依赖仍是表情识别网络面临的挑战.本文提出一种基于金字塔分割注意力的残差网络(Residual network based on pyramid split attention,PSA-ResNet)模型,该模型将ResNet50残差模块中的3×3卷积替换成金字塔分割注意力,以有效提取多尺度特征,增强跨通道语义信息的相关性.同时,为缩小同类表情之间的差异,扩大不同类表情之间的距离,在训练过程中引入了Softmax loss和Center loss联合损失函数优化模型参数.本文所提出的方法在Fer2013和CK+两个公开的数据集上进行仿真实验,分别取得了74.26%和98.35%的准确率,进一步证实了该方法相比前沿算法具有更好的表情识别效果.
An Expression Recognition Model Based on Pyramid Split Attention and Joint Loss
How to extract multi-scale features and model semantic dependencies between remote channels remains a challenge for expression recognition networks.This paper proposes a residual network based on pyramid split attention(PSA-ResNet),which replaces the 3×3 convolution in the ResNet50 residual module with PSA to effectively extract multi-scale features and enhance the correlation of cross channel information.In order to reduce the differences between similar expressions and expand the distance between different types of expressions,a joint loss function optimization parameter of Softmax loss and Center loss is introduced during the training process.The proposed model is simulated on two publicly available datasets,Fer2013 and CK+,and achieves accuracies of 74.26% and 98.35%,respectively,further confirming that this method has better recognition results compared to cutting-edge algorithms.
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