Disguised Faces in the Wild Competition

Image credit: geralt via Pixabay, CC0 Public Domain

With recent advancements in deep learning, the capabilities of automatic face recognition have been significantly increased. However, face recognition in an unconstrained environment with non-cooperative users is still a research challenge, pertinent for users such as law enforcement agencies. While several covariates such as pose, expression, illumination, aging, and low resolution have received significant attention, “disguise” is still considered an arduous covariate of face recognition.

Disguise as a covariate involves both intentional and unintentional changes on a face through which one can either obfuscate his/her identity or impersonate someone else’s identity. The problem can be further exacerbated due to an unconstrained environment or “in the wild” scenarios. However, disguise in the wild has not been studied in a comprehensive way, primarily due to unavailability of such a database.

As part of this workshop, we will conduct a competition in which participants will be asked to show their results on the proposed disguised faces in the wild (DFW) database. Top performing algorithms will be invited to submit their papers in the workshop and selected papers will be invited for presentation. Authors who have not participated in the competition can also submit the paper.


The competition will have two award winners in each of the following three categories:

  1. Impersonation (First place and Runner up)

  2. Obfuscation (First place and Runner up)

  3. Overall accuracy (First place and Runner up)

In total there will be six awards with monetary prizes as follows:

  • $6,000 USD awarded to the top scoring submission in each category

  • $2,500 USD awarded to the runner up in each category


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