Quo Vadis RankList-based System in Face Recognition?
Xinyi Zhang, Manuel G\"unther

TL;DR
This paper revisits and enhances RankList-based face recognition methods by integrating logits from a state-of-the-art network, significantly improving performance especially in low-data and diverse quality scenarios.
Contribution
The paper introduces Logit-Cohort Selection (LoCoS) to improve RankList-based face recognition by leveraging logits from advanced models instead of external cohorts.
Findings
Enhanced accuracy on challenging datasets
Significant performance improvements with LoCoS
Potential for future advancements in diverse conditions
Abstract
Face recognition in the wild has gained a lot of focus in the last few years, and many face recognition models are designed to verify faces in medium-quality images. Especially due to the availability of large training datasets with similar conditions, deep face recognition models perform exceptionally well in such tasks. However, in other tasks where substantially less training data is available, such methods struggle, especially when required to compare high-quality enrollment images with low-quality probes. On the other hand, traditional RankList-based methods have been developed that compare faces indirectly by comparing to cohort faces with similar conditions. In this paper, we revisit these RankList methods and extend them to use the logits of the state-of-the-art DaliFace network, instead of an external cohort. We show that through a reasonable Logit-Cohort Selection (LoCoS) the…
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 · Face and Expression Recognition
MethodsSparse Evolutionary Training · Focus
