  Purpose: Face detection is a needed component for the automatic analysis and
assistance of human activities during surgical procedures. Efficient face
detection algorithms can indeed help to detect and identify the persons present
in the room, and also be used to automatically anonymize the data. However,
current algorithms trained on natural images do not generalize well to the
operating room (OR) images. In this work, we provide a comparison of
state-of-the-art face detectors on OR data and also present an approach to
train a face detector for the OR by exploiting non-annotated OR images.
Methods: We propose a comparison of 6 state-of-the-art face detectors on
clinical data using Multi-View Operating Room Faces (MVOR-Faces), a dataset of
operating room images capturing real surgical activities. We then propose to
use self-supervision, a domain adaptation method, for the task of face
detection in the OR. The approach makes use of non-annotated images to
fine-tune a state-of-the-art detector for the OR without using any human
supervision. Results: The results show that the best model, namely the tiny
face detector, yields an average precision of 0.536 at Intersection over Union
(IoU) of 0.5. Our self-supervised model using non-annotated clinical data
outperforms this result by 9.2%. Conclusion: We present the first comparison of
state-of-the-art face detectors on operating room images and show that results
can be significantly improved by using self-supervision on non-annotated data.
