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Recent works on convolutional neural networks (CNNs) for facial alignmenthave demonstrated unprecedented accuracy on a variety of large, publiclyavailable datasets. However, the developed models are often both cumbersome andcomputationally expensive, and are not adapted to applications on resourcerestricted devices. In this work, we look into developing and training compactfacial alignment models that feature fast inference speed and small deploymentsize, making them suitable for applications on the aforementioned category ofdevices. Our main contribution lies in designing such small models whilemaintaining high accuracy of facial alignment. The models we propose make useof light CNN architectures adapted to the facial alignment problem for accuratetwo-stage prediction of facial landmark coordinates from low-resolution outputheatmaps. We further combine the developed facial tracker with a renderingmethod, and build a real-time makeup try-on demo that runs client-side insmartphone Web browsers. More results and demo are in our project page:http://research.modiface.com/makeup-try-on-cvprw2019/

Lightweight Real-time Makeup Try-on in Mobile Browsers with Tiny CNN Models for Facial Tracking