  Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective
for privacy protection; nevertheless, object recognition research typically
assumes access to complete, unobfuscated images. In this paper, we explore the
effects of face obfuscation on the popular ImageNet challenge visual
recognition benchmark. Most categories in the ImageNet challenge are not people
categories; however, many incidental people appear in the images, and their
privacy is a concern. We first annotate faces in the dataset. Then we
demonstrate that face obfuscation has minimal impact on the accuracy of
recognition models. Concretely, we benchmark multiple deep neural networks on
obfuscated images and observe that the overall recognition accuracy drops only
slightly (<= 1.0%). Further, we experiment with transfer learning to 4
downstream tasks (object recognition, scene recognition, face attribute
classification, and object detection) and show that features learned on
obfuscated images are equally transferable. Our work demonstrates the
feasibility of privacy-aware visual recognition, improves the highly-used
ImageNet challenge benchmark, and suggests an important path for future visual
datasets. Data and code are available at
https://github.com/princetonvisualai/imagenet-face-obfuscation.
