Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container
This work provides an incremental improvement by offering a more accurate and user-friendly face detection tool for researchers in fields like computational psychology and behavioral imaging who lack computer vision expertise.
The authors tackled the problem of making state-of-the-art face detection more accessible by introducing Dockerface, a Faster R-CNN face detector in a Docker container that requires no training and is easy to install and use, addressing the reliance on outdated detectors like dlib and OpenCV Haar due to their simplicity.
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.