评测真实场景下人脸检测与开放集识别，发现大模型预训练效果更好。
Watchlist Challenge: 3rd Open-set Face Detection and Identification
- 用增强版UCCS数据集和新评估协议测试4个检测+9个识别系统
- 检测性能普遍稳健，闭集识别差异大，大模型预训练优势明显
- 开放集识别在高召回率时仍需提升，适合关注实际应用的开发者
在生物识别与监控的当前背景下，准确识别非受控环境中的面部至关重要。Watchlist Challenge聚焦于真实监控场景中的人脸检测与开放集识别这一关键需求。本文通过增强版未受控大学生数据集（UCCS）及新评估协议，全面评估了参与算法的表现。共有4个团队提交了4个面部检测系统和9个开放集人脸识别系统。评估结果表明，尽管检测能力总体稳健，但闭集识别性能差异显著，采用大规模数据预训练的模型表现更优。然而，在开放集场景下，尤其在较高真阳性识别率（即较低阈值）时，仍需进一步改进。
原文摘要 · Abstract (English)
In the current landscape of biometrics and surveillance, the ability to accurately recognize faces in uncontrolled settings is paramount. The Watchlist Challenge addresses this critical need by focusing on face detection and open-set identification in real-world surveillance scenarios. This paper presents a comprehensive evaluation of participating algorithms, using the enhanced UnConstrained College Students (UCCS) dataset with new evaluation protocols. In total, four participants submitted four face detection and nine open-set face recognition systems. The evaluation demonstrates that while detection capabilities are generally robust, closed-set identification performance varies significantly, with models pre-trained on large-scale datasets showing superior performance. However, open-set scenarios require further improvement, especially at higher true positive identification rates, i.e., lower thresholds.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。