FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition
This addresses the problem of insufficient training data for facial expression recognition, though it is an incremental improvement over existing fine-tuning approaches.
The paper tackles the challenge of training deep networks for facial expression recognition with limited data by introducing FaceNet2ExpNet, which regularizes an expression network using a pre-trained face recognition network. The method achieves state-of-the-art results on four public expression databases (CK+, Oulu-CASIA, TFD, and SFEW).
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.