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.

本研究提出了一种名为 FaceNet2ExpNet 的新思想，通过静态图片训练表情识别网络，先预训练表情网络的卷积层，再添加全连接层联合训练，通过可视化结果表明该模型能够捕捉到更高层次的表情语义，实验证明该方法优于现有技术在四个公共数据库上的识别结果表现。