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Relatively small data sets available for expression recognition research makethe training of deep networks for expression recognition very challenging.Although fine-tuning can partially alleviate the issue, the performance isstill below acceptable levels as the deep features probably contain redun- dantinformation from the pre-trained domain. In this paper, we presentFaceNet2ExpNet, a novel idea to train an expression recognition network basedon static images. We first propose a new distribution function to model thehigh-level neurons of the expression network. Based on this, a two-stagetraining algorithm is carefully designed. In the pre-training stage, we trainthe convolutional layers of the expression net, regularized by the face net; Inthe refining stage, we append fully- connected layers to the pre-trainedconvolutional layers and train the whole network jointly. Visualization showsthat the model trained with our method captures improved high-level expressionsemantics. Evaluations on four public expression databases, CK+, Oulu-CASIA,TFD, and SFEW demonstrate that our method achieves better results thanstate-of-the-art.

FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition