基于深度学习的人脸表情识别系统研究

Research on Facial Expression Recognition System Based on Deep Learning

针对深度卷积神经网络随着卷积层数增加而导致网络模型难以训练和性能退化等问题,提出了一种基于深度残差网络的人脸表情识别方法。采用改进后的ResNet18模型,结合数据增强、mixup、label smoothing等辅助策略对FER2013训练集进行300个epoch的训练,利用最优的权重,在FER2013的验证数据集上达到了72.09%的准确率;并结合YOLOv5Face预训练权重,实现了人脸检测和表情识别。

Aiming at the problems of network model training difficulty and performance degradation due to the increase of convolutional layers in deep convolutional neural network,a facial expression recognition method based on deep residual network is proposed.By using the improved ResNet18 model and combining auxiliary strategies such as data enhancement,mixup and label Smoothing,FER2013 training sets are trained for 300 epoch.Using the optimal weights,the accuracy of FER2013 validation data set reaches 72.09%.Combined with the weight of YOLOv5Face pre-training,face detection and expression recognition are realized.