De-identification Without Losing Faces
Yuezun Li, Siwei Lyu · 2019
Training of deep learning models for computer vision requires large image or video datasets from real world. Often, in collecting such datasets, we also need to protect the privacy of the people captured in the images or videos, while still preserve useful attributes such as facial expressions. In this work, we describe a new face de-identification method to achieve this, which is based on a face attribute transfer model (FATM). FATM is a deep neural network model trained to map non-identity related facial attributes to the face of donors, who are a small number of consented subjects. Using the donors' faces ensures the natural appearance of the synthesized faces, and 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.