  Recent progress in face detection (including keypoint detection), and
recognition is mainly being driven by (i) deeper convolutional neural network
architectures, and (ii) larger datasets. However, most of the large datasets
are maintained by private companies and are not publicly available. The
academic computer vision community needs larger and more varied datasets to
make further progress.
  In this paper we introduce a new face dataset, called UMDFaces, which has
367,888 annotated faces of 8,277 subjects. We also introduce a new face
recognition evaluation protocol which will help advance the state-of-the-art in
this area. We discuss how a large dataset can be collected and annotated using
human annotators and deep networks. We provide human curated bounding boxes for
faces. We also provide estimated pose (roll, pitch and yaw), locations of
twenty-one key-points and gender information generated by a pre-trained neural
network. In addition, the quality of keypoint annotations has been verified by
humans for about 115,000 images. Finally, we compare the quality of the dataset
with other publicly available face datasets at similar scales.
