  Recent works on convolutional neural networks (CNNs) for facial alignment
have demonstrated unprecedented accuracy on a variety of large, publicly
available datasets. However, the developed models are often both cumbersome and
computationally expensive, and are not adapted to applications on resource
restricted devices. In this work, we look into developing and training compact
facial alignment models that feature fast inference speed and small deployment
size, making them suitable for applications on the aforementioned category of
devices. Our main contribution lies in designing such small models while
maintaining high accuracy of facial alignment. The models we propose make use
of light CNN architectures adapted to the facial alignment problem for accurate
two-stage prediction of facial landmark coordinates from low-resolution output
heatmaps. We further combine the developed facial tracker with a rendering
method, and build a real-time makeup try-on demo that runs client-side in
smartphone Web browsers. More results and demo are in our project page:
http://research.modiface.com/makeup-try-on-cvprw2019/
