论文标题
面孔对人的重新识别有多重要？
How important are faces for person re-identification?
论文作者
论文摘要
本文调查了现有的最先进的人重新识别模型对人脸的存在和可见性的依赖性。我们应用面部检测和模糊算法来创建几个流行人士重新识别数据集的匿名版本，包括Market1501，Dukemtmc-Reid，Cuhk03，Viper和Airport。使用准确性和计算效率范围的现有最新模型的横截面，我们评估了这种匿名化对使用标准指标重新识别性能的影响。也许令人惊讶的是，对地图的效果非常小，并且通过简单地训练数据的匿名版本而不是原始数据来恢复准确性。这些发现在多个模型和数据集之间是一致的。这些结果表明，数据集可以通过模糊的面孔安全地匿名化，而不会显着影响人员的重新识别系统的性能，并且可以释放以前存在隐私或数据保护问题的新富裕重新识别数据集。
This paper investigates the dependence of existing state-of-the-art person re-identification models on the presence and visibility of human faces. We apply a face detection and blurring algorithm to create anonymized versions of several popular person re-identification datasets including Market1501, DukeMTMC-reID, CUHK03, Viper, and Airport. Using a cross-section of existing state-of-the-art models that range in accuracy and computational efficiency, we evaluate the effect of this anonymization on re-identification performance using standard metrics. Perhaps surprisingly, the effect on mAP is very small, and accuracy is recovered by simply training on the anonymized versions of the data rather than the original data. These findings are consistent across multiple models and datasets. These results indicate that datasets can be safely anonymized by blurring faces without significantly impacting the performance of person reidentification systems, and may allow for the release of new richer re-identification datasets where previously there were privacy or data protection concerns.
