基于元数据的冒名顶替者对分数变化的理解

A. Sgroi, K. Bowyer, P. Flynn
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引用次数: 1

摘要

关于给定人脸图像的元数据可以包括诸如受试者的出生年份、受试者性别和采集日期等信息。通过确定图库和探测图像之间的元数据匹配程度(例如两个具有相同性别的主题),我们假设与冒名顶替图像对匹配的元数据值越多,就会增加错误匹配的可能性。在这项工作中,我们探讨了出生年份、性别、获得日期和表达,试图理解由冒充者图像对产生的匹配分数的变化。以前开发的SNoW技术所识别的弱分区中的冒名顶替者对具有略多的匹配元数据值。然而,具有更多匹配元数据的图像对在强和弱冒充者对之间产生的分数几乎没有统计学上的显著差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Metadata-based understanding of impostor pair score variations
Metadata about a given face image can include information such as the subject's year of birth, subject gender, and date of acquisition. By determining the degree of metadata matches between the gallery and probe images (such as two subjects having the same gender) we hypothesize that more metadata values that match for an impostor image pair increases the likelihood of a false match. In this work, we explore year of birth, gender, date of acquisition, and expression in an attempt to understand variations in match scores produced by impostor image pairs. Impostor pairs that fall in the weak partition as identified by the previously developed SNoW technique have a slightly larger number of matching metadata values. However, there is little to no statistically significant difference in the scores produced by image pairs with more matching metadata between strong and weak impostor pairs.
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