Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container
Abstract
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.
Cite
@article{arxiv.1708.04370,
title = {Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container},
author = {Nataniel Ruiz and James M. Rehg},
journal= {arXiv preprint arXiv:1708.04370},
year = {2018}
}