AU (Action Unit) detection based on BEGAN data augmentation

摘要

Limited annotated training data is a challenging problem in Action Unit detection. Particularly, for micro-expression AU detection, more training data can help improve the performance of detection. For the purpose of data augmentation, this paper put to use the generative adversarial networks (GAN) which is able to generate High-quality pictures that as a supplementary to our limited database. In addition, we propose a sample and effective model for facial micro-expression action units (AU) detection based on 3D-CNNs and Gated Recurrent Unit (GRU) network. The network is composed of 6 layers including 3 convolutional layers, correspondingly, each convolution layer is followed by a pooling layer, and a single layer GRU unit with 15 hidden nodes. For the task of recognizing AUs, we have trained a network for the DISFA datasets, where the GAN applied on, so as to take full advantage of AU-tagged databases and enable the network convergence faster and easier. We show that our model and the method supplying labeled-AU database achieve competitive performance compared with state-of-the-art deep learning methods and traditional data expansion methods such as rotate angles and increase noise based on original drawings.