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Recent progress in face detection (including keypoint detection), andrecognition is mainly being driven by (i) deeper convolutional neural networkarchitectures, and (ii) larger datasets. However, most of the large datasetsare maintained by private companies and are not publicly available. Theacademic computer vision community needs larger and more varied datasets tomake further progress.  In this paper we introduce a new face dataset, called UMDFaces, which has367,888 annotated faces of 8,277 subjects. We also introduce a new facerecognition evaluation protocol which will help advance the state-of-the-art inthis area. We discuss how a large dataset can be collected and annotated usinghuman annotators and deep networks. We provide human curated bounding boxes forfaces. We also provide estimated pose (roll, pitch and yaw), locations oftwenty-one key-points and gender information generated by a pre-trained neuralnetwork. In addition, the quality of keypoint annotations has been verified byhumans for about 115,000 images. Finally, we compare the quality of the datasetwith other publicly available face datasets at similar scales.

UMDFaces: An Annotated Face Dataset for Training Deep Networks