  Face detection is a very important task and a necessary pre-processing step
for many applications such as facial landmark detection, pose estimation,
sentiment analysis and face recognition. Not only is face detection an
important pre-processing step in computer vision applications but also in
computational psychology, behavioral imaging and other fields where researchers
might not be initiated in computer vision frameworks and state-of-the-art
detection applications. A large part of existing research that includes face
detection as a pre-processing step uses existing out-of-the-box detectors such
as the HoG-based dlib and the OpenCV Haar face detector which are no longer
state-of-the-art - they are primarily used because of their ease of use and
accessibility. We introduce Dockerface, a very accurate Faster R-CNN face
detector in a Docker container which requires no training and is easy to
install and use.
