基于轻量卷积网络多层特征融合的人脸表情识别

Facial Expression Recognition by Merging Multilayer Features of Lightweight Convolutional Networks

基于深度学习的表情识别方法存在参数量大、实时性差等问题,针对此问题,提出一种基于轻量卷积网络的多层特征融合的人脸表情识别方法。首先使用改进的倒置残差网络为基本单元搭建轻量卷积网络模型,然后采用池化、1×1卷积、全局平均池化法筛选卷积网络中的浅层特征,并对这些筛选的浅层特征与深层特征进行融合用于表情识别。在两个常用的真实表情数据集RAF-DB和AffectNet上对所提方法进行测试,识别准确率分别达85.49%和57.70%,且模型参数量仅有0.2×10^(6)。

Current methods of expression recognition based on deep learning exhibit problems such as a large number of parameters and poor real-time performance.To resolve these problems,this paper proposes a facial expression recognition method by merging the multilayer features of a lightweight convolutional network.First,the improved inverted residual network was considered as the basic unit for building the lightweight convolutional network model.Then,the shallow features associated with the convolutional network subjected to the methods of pooling,1×1 convolution,and global average pooling were merged with the deep features of the network for expression recognition tasks.The proposed method was verified by employing RAF-DB and AffectNet,which are commonly used expression datasets.Results indicate that the facial expression recognition accuracies of the proposed method when considering the RAF-DB and AffectNet datasets are 85.49%and 57.70%,respectively.Furthermore,the number of model parameters is only 0.2×10^(6).