Posted By: Intelligence Advanced Research Project Activity
Partners: University of Maryland, IBM, IIIT-Delhi Skill: Software/Apps Interest: Science & Research Submission Dates: 12 a.m. ET, Jan 20, 2018 - 11:59 p.m. ET, May 01, 2018 Winners Announced: May 10, 2018
With recent advancements in deep learning, the capabilities of automatic face recognition has been significantly increased. However, face recognition in 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 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 as database. As part of 1st International Workshop on Disguised Face in the Wild at CVPR 2018, a competition is being held in which participants are asked to show their results on the Disguised Faces in the Wild (DFW) database. More details on the workshop and competition are available at:
Prize awards for the DFW 2018 Competition are provided by the Intelligence Advanced Research Projects Activity (IARPA), within the Office of the Director of National Intelligence (ODNI).
Two phases of the competition and winners:
The competition involves two phases. The early phase involves the participants with early access to the data with an opportunity to submit a paper describing their approach. The second phase participants will have more time to submit their score results but will not be able to submit a written paper. However, the second phase participants may be invited to orally present their work based on their performance results at the CVPR workshop. Teams may elect to participate in either or both phases.
- Enrollment deadline for the overall competition: February 23, 2018
- Result Submissions due to Organizers: March 05, 2018 UPDATED
- Invitation to top performing participants for paper submission (results will not be released publicly): Mar 07, 2018
- Paper submissions by selected Competition Participants due to organizers: Mar 20, 2018
- Notification provided authors: Apr 05, 2018
- Camera-ready deadline: Apr 09, 2018
- Final results submissions to Organizers: May 01, 2018
- Winners of the competition announced based on final Comparative Results: May 10, 2018
Results will be announced at the end of Phase 2 based on submission received up until the May 1st deadline. Participants are permitted to submit one set of results to Phase 1 and then resubmit a second set of revised/final results to Phase 2. No paper submission is possible for participants who only participate in Phase 2, but winners will be invited to give an oral presentation on their work at the CVPR workshop.
In case of any difficulties or questions, please email to firstname.lastname@example.org.
A full description of registration, rules, submissions, and evaluations are at: http://iab-rubric.org/DFW/dfw.html.
The DFW dataset consists of 1000 subject and total of 11155 images. Out of this dataset, 400 subjects comprise the training set and 600 subjects comprise the testing set. The subject folder consists of a subject, disguised, and impersonator images. Access to the DFW dataset is granted to participants after enrollment through the DFW website.
- Participants’ are required to generate similarity scores (a larger value indicates greater similarity) from the biometric matchers. If a participant’s matcher generates a dissimilarity score instead of a similarity score, the scores should be negated or inverted in some way so that the resulting value is a similarity measure. Participants in the competition have been provided with the testing set. From the data, the participants are required to generate and submit similarity matrices of size 7771 X 7771, the size of the testing data. The ordering of test images is same in both row and column. The (i,j) entry of a similarity matrix is the similarity score generated by the algorithm when supplied an image i from the testing set and query entry j as a probe sample. Entry (i,i) as it corresponds to matching the same image.
- Participants are also required to submit the score matrix on the training database. The ordering should be exactly same as the order given in the text file containing subject names.
- Participants are required to submit the matrices along with the companion data for the corresponding 1,000 points ROC curves. The match scores computed with validation and disguised images will comprise the genuine scores. Impostor scores will include match scores generated from impersonator images as well as cross subject matching scores.
- While it is not mandatory, we also encourage the participants to submit their models/executable/API for verification of the results.
- The participants can choose to remain anonymous in the analysis and report. Participants must explicitly make this request; the default position will be to associate results with participants.
A full description of rules is available at: http://iab-rubric.org/DFW/dfw.html.