Diversity in Faces
Michele Merler, Nalini Ratha, Rogerio S. Feris, John R. Smith

TL;DR
This paper introduces the Diversity in Faces dataset with one million annotated images, using established facial coding schemes to advance research on facial diversity, fairness, and accuracy in face recognition systems.
Contribution
The paper presents a large-scale face dataset annotated with scientifically grounded facial coding schemes to facilitate research on facial diversity and fairness in AI.
Findings
Provides a dataset of one million annotated face images.
Uses ten well-established facial coding schemes for annotations.
Aims to improve fairness and accuracy in face recognition research.
Abstract
Face recognition is a long standing challenge in the field of Artificial Intelligence (AI). The goal is to create systems that accurately detect, recognize, verify, and understand human faces. There are significant technical hurdles in making these systems accurate, particularly in unconstrained settings due to confounding factors related to pose, resolution, illumination, occlusion, and viewpoint. However, with recent advances in neural networks, face recognition has achieved unprecedented accuracy, largely built on data-driven deep learning methods. While this is encouraging, a critical aspect that is limiting facial recognition accuracy and fairness is inherent facial diversity. Every face is different. Every face reflects something unique about us. Aspects of our heritage - including race, ethnicity, culture, geography - and our individual identify - age, gender, and other visible…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsFace recognition and analysis
