  The seven basic facial expression classifications are a basic way to express
complex human emotions and are an important part of artificial intelligence
research. Based on the traditional Bayesian neural network framework, the
ResNet18_BNN network constructed in this paper has been improved in the
following three aspects: (1) A new objective function is proposed, which is
composed of the KL loss of uncertain parameters and the intersection of
specific parameters. Entropy loss composition. (2) Aiming at a special
objective function, a training scheme for alternately updating these two
parameters is proposed. (3) Only model the parameters of the last convolution
group. Through testing on the FER2013 test set, we achieved 71.5% and 73.1%
accuracy in PublicTestSet and PrivateTestSet, respectively. Compared with
traditional Bayesian neural networks, our method brings the highest
classification accuracy gain.
