  In this paper, we propose a deep learning based approach for facial action
unit detection by enhancing and cropping the regions of interest. The approach
is implemented by adding two novel nets (layers): the enhancing layers and the
cropping layers, to a pretrained CNN model. For the enhancing layers, we
designed an attention map based on facial landmark features and applied it to a
pretrained neural network to conduct enhanced learning (The E-Net). For the
cropping layers, we crop facial regions around the detected landmarks and
design convolutional layers to learn deeper features for each facial region
(C-Net). We then fuse the E-Net and the C-Net to obtain our Enhancing and
Cropping (EAC) Net, which can learn both feature enhancing and region cropping
functions. Our approach shows significant improvement in performance compared
to the state-of-the-art methods applied to BP4D and DISFA AU datasets.
