  Relatively small data sets available for expression recognition research make
the training of deep networks for expression recognition very challenging.
Although fine-tuning can partially alleviate the issue, the performance is
still below acceptable levels as the deep features probably contain redun- dant
information from the pre-trained domain. In this paper, we present
FaceNet2ExpNet, a novel idea to train an expression recognition network based
on static images. We first propose a new distribution function to model the
high-level neurons of the expression network. Based on this, a two-stage
training algorithm is carefully designed. In the pre-training stage, we train
the convolutional layers of the expression net, regularized by the face net; In
the refining stage, we append fully- connected layers to the pre-trained
convolutional layers and train the whole network jointly. Visualization shows
that the model trained with our method captures improved high-level expression
semantics. Evaluations on four public expression databases, CK+, Oulu-CASIA,
TFD, and SFEW demonstrate that our method achieves better results than
state-of-the-art.
