  With the development of deep learning, the structure of convolution neural
network is becoming more and more complex and the performance of object
recognition is getting better. However, the classification mechanism of
convolution neural networks is still an unsolved core problem. The main problem
is that convolution neural networks have too many parameters, which makes it
difficult to analyze them. In this paper, we design and train a convolution
neural network based on the expression recognition, and explore the
classification mechanism of the network. By using the Deconvolution
visualization method, the extremum point of the convolution neural network is
projected back to the pixel space of the original image, and we qualitatively
verify that the trained expression recognition convolution neural network forms
a detector for the specific facial action unit. At the same time, we design the
distance function to measure the distance between the presence of facial
feature unit and the maximal value of the response on the feature map of
convolution neural network. The greater the distance, the more sensitive the
feature map is to the facial feature unit. By comparing the maximum distance of
all facial feature elements in the feature graph, the mapping relationship
between facial feature element and convolution neural network feature map is
determined. Therefore, we have verified that the convolution neural network has
formed a detector for the facial Action unit in the training process to realize
the expression recognition.
