Face detection in the operating room: Comparison of state-of-the-art methods and a self-supervised approach

Vinkle Srivastav, Thibaut Issenhuth, Afshin Gangi, Nicolas Padoy

Abstract:

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 0.556 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