  Research in face recognition has seen tremendous growth over the past couple
of decades. Beginning from algorithms capable of performing recognition in
constrained environments, the current face recognition systems achieve very
high accuracies on large-scale unconstrained face datasets. While upcoming
algorithms continue to achieve improved performance, a majority of the face
recognition systems are susceptible to failure under disguise variations, one
of the most challenging covariate of face recognition. Most of the existing
disguise datasets contain images with limited variations, often captured in
controlled settings. This does not simulate a real world scenario, where both
intentional and unintentional unconstrained disguises are encountered by a face
recognition system. In this paper, a novel Disguised Faces in the Wild (DFW)
dataset is proposed which contains over 11000 images of 1000 identities with
different types of disguise accessories. The dataset is collected from the
Internet, resulting in unconstrained face images similar to real world
settings. This is the first-of-a-kind dataset with the availability of
impersonator and genuine obfuscated face images for each subject. The proposed
dataset has been analyzed in terms of three levels of difficulty: (i) easy,
(ii) medium, and (iii) hard in order to showcase the challenging nature of the
problem. It is our view that the research community can greatly benefit from
the DFW dataset in terms of developing algorithms robust to such adversaries.
The proposed dataset was released as part of the First International Workshop
and Competition on Disguised Faces in the Wild at CVPR, 2018. This paper
presents the DFW dataset in detail, including the evaluation protocols,
baseline results, performance analysis of the submissions received as part of
the competition, and three levels of difficulties of the DFW challenge dataset.
