用先进模型的输出优化传统排序方法，提升低质图像识别效果
Quo Vadis RankList-based System in Face Recognition?
- 用DaliFace模型的logits替代外部参照组构建排序列表
- 在两个数据集上显著提升低质量图像识别准确率
- 适合处理高低质量图像对比的实用场景
野外人脸识别人脸识别近年来受到广泛关注，许多模型针对中等质量图像设计，得益于大规模训练数据，表现优异。然而在训练数据较少、需比对高质量注册图像与低质量探测图像的任务中，现有方法表现不佳。传统基于排序列表（RankList）的方法通过与条件相似的参考人脸比较间接实现识别。本文重新审视此类方法，将其扩展为使用最先进的DaliFace网络的logits作为参考，而非外部参照组。通过合理的Logit-Cohort Selection（LoCoS）策略，显著提升了排序列表函数的性能。在两个具有挑战性的面部识别数据集上的实验不仅验证了所提方法的有效性，也为未来应对图像质量差异的研究奠定了基础。
原文摘要 · Abstract (English)
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.
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