  The flow-based generative model is a deep learning generative model, which
obtains the ability to generate data by explicitly learning the data
distribution. Theoretically its ability to restore data is stronger than other
generative models. However, its implementation has many limitations, including
limited model design, too many model parameters and tedious calculation. In
this paper, a bi-parallel linear flow model for facial emotion generation from
emotion set images is constructed, and a series of improvements have been made
in terms of the expression ability of the model and the convergence speed in
training. The model is mainly composed of several coupling layers superimposed
to form a multi-scale structure, in which each coupling layer contains 1*1
reversible convolution and linear operation modules. Furthermore, this paper
sorted out the current public data set of facial emotion images, made a new
emotion data, and verified the model through this data set. The experimental
results show that, under the traditional convolutional neural network, the
3-layer 3*3 convolution kernel is more conducive to extracte the features of
the face images. The introduction of principal component decomposition can
improve the convergence speed of the model.
