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在自动面部识别环境中,针对身份不明人员数据存储库的潜在应用,人口统计学改变后的计算机面部近似图像的可识别性。

Recognizability of Demographically Altered Computerized Facial Approximations in an Automated Facial Recognition Context for Potential Application in Unidentified Persons Data Repositories.

作者信息

Parks Connie L, Monson Keith L

机构信息

FBI Laboratory Division, 2501 Investigation Parkway, Quantico, VA 22135, USA.

出版信息

Biology (Basel). 2023 May 4;12(5):682. doi: 10.3390/biology12050682.

DOI:10.3390/biology12050682
PMID:37237496
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10215877/
Abstract

This study examined the recognizability of demographically altered facial approximations for potential utility in unidentified persons tracking systems. Five computer-generated approximations were generated for each of 26 African male participants using the following demographic parameters: (i) African male (true demographics), (ii) African female, (iii) Caucasian male, (iv) Asian male, and (v) Hispanic male. Overall, 62% of the true demographic facial approximations for the 26 African male participants examined were matched to a corresponding life photo within the top 50 images of a candidate list generated from an automated blind search of an optimally standardized gallery of 6159 photographs. When the African male participants were processed as African females, the identification rate was 50%. In contrast, less congruent identification rates were observed when the African male participants were processed as Caucasian (42%), Asian (35%), and Hispanic (27%) males. The observed results suggest that approximations generated using the opposite sex may be operationally informative if sex is unknown. The performance of approximations generated using alternative ancestry assignments, however, was less congruent with the performance of the true demographic approximation (African male) and may not yield as operationally constructive data as sex-altered approximations.

摘要

本研究考察了人口统计学特征改变后的面部近似图像在身份不明人员追踪系统中的潜在效用及可识别性。使用以下人口统计学参数,为26名非洲男性参与者中的每一位生成了5种计算机生成的近似图像:(i)非洲男性(真实人口统计学特征),(ii)非洲女性,(iii)白人男性,(iv)亚洲男性,以及(v)西班牙裔男性。总体而言,在对6159张照片进行最优标准化处理后自动盲目搜索生成的候选列表的前50张图像中,所考察的26名非洲男性参与者的真实人口统计学特征面部近似图像中有62%与相应的生活照片匹配。当将非洲男性参与者视为非洲女性进行处理时,识别率为50%。相比之下,当将非洲男性参与者视为白人男性(42%)、亚洲男性(35%)和西班牙裔男性(27%)进行处理时,观察到的识别率一致性较低。观察结果表明,如果性别未知,使用异性生成的近似图像在操作上可能具有参考价值。然而,使用替代种族归属生成的近似图像的表现与真实人口统计学特征近似图像(非洲男性)的表现一致性较低,可能无法产生与性别改变后的近似图像一样具有操作建设性的数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a22/10215877/76601e240696/biology-12-00682-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a22/10215877/76601e240696/biology-12-00682-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a22/10215877/76601e240696/biology-12-00682-g001.jpg

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A comparison of proteomic, genomic, and osteological methods of archaeological sex estimation.考古学性别估计的蛋白质组学、基因组学和骨骼学方法比较。
Sci Rep. 2020 Jul 17;10(1):11897. doi: 10.1038/s41598-020-68550-w.
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Recognizability of computer-generated facial approximations in an automated facial recognition context for potential use in unidentified persons data repositories: Optimally and operationally modeled conditions.
在潜在用于身份不明人员数据库的自动化人脸识别环境中,计算机生成面部近似物的可识别性:最佳和操作模型条件。
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Assessment of accuracy and recognition of three-dimensional computerized forensic craniofacial reconstruction.三维计算机法医颅面重建的准确性和识别评估。
PLoS One. 2018 May 2;13(5):e0196770. doi: 10.1371/journal.pone.0196770. eCollection 2018.
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Biometric correspondence between reface computerized facial approximations and CT-derived ground truth skin surface models objectively examined using an automated facial recognition system.使用自动面部识别系统客观检查reface计算机化面部近似与CT衍生的地面真实皮肤表面模型之间的生物特征对应关系。
Forensic Sci Int. 2018 May;286:8-11. doi: 10.1016/j.forsciint.2018.02.019. Epub 2018 Feb 27.
6
Automated facial recognition of manually generated clay facial approximations: Potential application in unidentified persons data repositories.手工制作的黏土面部近似模型的自动面部识别:在身份不明人员数据库中的潜在应用。
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Accuracy Rates of Ancestry Estimation by Forensic Anthropologists Using Identified Forensic Cases.法医人类学家利用已确认法医案例进行祖先估计的准确率
J Forensic Sci. 2017 Jul;62(4):971-974. doi: 10.1111/1556-4029.13361. Epub 2017 Jan 30.
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J Forensic Sci. 2016 Sep;61(5):1307-10. doi: 10.1111/1556-4029.13137. Epub 2016 Jun 29.
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