Disguised face recognition has wide-spread applicability in scenarios such as law enforcement, surveillance, and access control. Disguise accessories such as sunglasses, masks, scarves, or make-up modify or occlude different facial regions which makes face recognition a challenging task. In order to understand and benchmark the state-of-the-art on face recognition in the presence of disguise variations, the Disguised Faces in the Wild 2019 (DFW2019) competition has been organized. This paper summarizes the outcome of the competition in terms of the dataset used for evaluation, a brief review of the algorithms employed by the participants for this task, and the results obtained. The DFW2019 dataset has been released with four evaluation protocols and baseline results obtained from two deep learning-based state-of-the-art face recognition models. The DFW2019 dataset has also been analyzed with respect to degrees of difficulty: (i) easy, (ii) medium, and (iii) hard. The dataset has been released as part of the International Workshop on Disguised Faces in the Wild at International Conference on Computer Vision (ICCV), 2019. © 2019 IEEE.