Introducing Human Mandible Identification [(hu)MANid]: A Free, Web‐Based GUI to Classify Human Mandibles
Abstract
Abstract Statistical programs have revolutionized the way in which forensic anthropologists conduct casework by allowing practitioners to use computationally complex analytics at the click of a button. Importantly, the products of these statistical programs are reproducible and contain measures of error or uncertainty, thereby strengthening conclusions. This paper is an introduction to (hu) MAN id, a free, web‐based application that uses linear and mixture discriminant function analyses to classify human mandibles into one of many worldwide and/or periodic reference groups. The mechanics, development, and use of the application will be discussed. Further, the program was tested against other software to compare model performances and classifications. Total correct classifications among the test cases and programs were identical. Ten mandibles were tested using both statistical procedures. Mixture discriminant analysis improved classification by an average of 9.3% and correctly identified three more mandibles than LDA . Therefore, we believe (hu) MAN id will be an asset to the anthropological community.
- Authors:
-
- Central Identification Laboratory Defense POW/MIA Accounting Agency 590 Moffet Street JBPHH HI
- Central Identification Laboratory Defense POW/MIA Accounting Agency 590 Moffet Street JBPHH HI, Department of Anatomy University of Pretoria Private Bag x323 0007 Arcadia South Africa
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1401276
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- Journal of Forensic Sciences
- Additional Journal Information:
- Journal Name: Journal of Forensic Sciences Journal Volume: 62 Journal Issue: 6; Journal ID: ISSN 0022-1198
- Publisher:
- Wiley-Blackwell
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Berg, Gregory E., and Kenyhercz, Michael W. Introducing Human Mandible Identification [(hu)MANid]: A Free, Web‐Based GUI to Classify Human Mandibles. United States: N. p., 2017.
Web. doi:10.1111/1556-4029.13479.
Berg, Gregory E., & Kenyhercz, Michael W. Introducing Human Mandible Identification [(hu)MANid]: A Free, Web‐Based GUI to Classify Human Mandibles. United States. https://doi.org/10.1111/1556-4029.13479
Berg, Gregory E., and Kenyhercz, Michael W. Mon .
"Introducing Human Mandible Identification [(hu)MANid]: A Free, Web‐Based GUI to Classify Human Mandibles". United States. https://doi.org/10.1111/1556-4029.13479.
@article{osti_1401276,
title = {Introducing Human Mandible Identification [(hu)MANid]: A Free, Web‐Based GUI to Classify Human Mandibles},
author = {Berg, Gregory E. and Kenyhercz, Michael W.},
abstractNote = {Abstract Statistical programs have revolutionized the way in which forensic anthropologists conduct casework by allowing practitioners to use computationally complex analytics at the click of a button. Importantly, the products of these statistical programs are reproducible and contain measures of error or uncertainty, thereby strengthening conclusions. This paper is an introduction to (hu) MAN id, a free, web‐based application that uses linear and mixture discriminant function analyses to classify human mandibles into one of many worldwide and/or periodic reference groups. The mechanics, development, and use of the application will be discussed. Further, the program was tested against other software to compare model performances and classifications. Total correct classifications among the test cases and programs were identical. Ten mandibles were tested using both statistical procedures. Mixture discriminant analysis improved classification by an average of 9.3% and correctly identified three more mandibles than LDA . Therefore, we believe (hu) MAN id will be an asset to the anthropological community.},
doi = {10.1111/1556-4029.13479},
journal = {Journal of Forensic Sciences},
number = 6,
volume = 62,
place = {United States},
year = {Mon Mar 06 00:00:00 EST 2017},
month = {Mon Mar 06 00:00:00 EST 2017}
}
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https://doi.org/10.1111/1556-4029.13479
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