Quo Vadis RankList-based System in Face Recognition?

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 performance of RankList-based functions can be improved drastically. Experiments on two challenging face recognition datasets not only demonstrate the enhanced performance of our proposed method but also set the stage for future advancements in handling diverse image qualities.