基于改进的深度残差网络的表情识别研究.

This paper proposed an improved residual network (ResNet) expression recognition algorithm. The algorithm used small convolution kernels and a deep network structure to solve the problem of accuracy reduction with the increase of depth by the residual module. The experiment overcame the shortcoming of insufficient data through transfer learning, which could effectively prevent overfitting. The network architecture used a linear SVM for classification. The experiment used the ImageNet database to pre-train network parameters to have an excellent ability to extract feature. According to transfer learning, the algorithm used the FER-2013 database and the expanded CK + database to fine-tune and train network parameters, and overcame the problem that shallow networks rely on manual features and deep networks were difficult to train. The results show the recognition rates is 91.333% and 95.775% on the CK + database and the GENKI-4K database,respectively. The classification accuracy of SVM in CK + database is about 1% higher than that of softmax. [ABSTRACT FROM AUTHOR]