  Training of deep learning models for computer vision requires large image or
video datasets from real world. Often, in collecting such datasets, we need to
protect the privacy of the people captured in the images or videos, while still
preserve the useful attributes such as facial expressions. In this work, we
describe a new face de-identification method that can preserve essential facial
attributes in the faces while concealing the identities. Our method takes
advantage of the recent advances in face attribute transfer models, while
maintaining a high visual quality. Instead of changing factors of the original
faces or synthesizing faces completely, our method use a trained facial
attribute transfer model to map non-identity related facial attributes to the
face of donors, who are a small number (usually 2 to 3) of consented subjects.
Using the donors' faces ensures that the natural appearance of the synthesized
faces, while ensuring the identity of the synthesized faces are changed. On the
other hand, the FATM blends the donors' facial attributes to those of the
original faces to diversify the appearance of the synthesized faces.
Experimental results on several sets of images and videos demonstrate the
effectiveness of our face de-ID algorithm.
