Determining the sexual identities of prehistoric cave artists using digitized handprints: A machine learning approach

James Z. Wang, Weina Ge, Dean R. Snow, Prasenjit Mitra, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

The sexual identities of human handprints inform hypotheses regarding the roles of males and females in prehistoric contexts. Sexual identity has previously been manually determined by measuring the ratios of the lengths of the individual's fingers as well as by using other physical features. Most conventional studies measure the lengths manually and thus are often constrained by the lack of scaling information on published images. We have created a method that determines sex by applying modern machine-learning techniques to relative measures obtained from images of human hands. This is the known attempt at substituting automated methods for time-consuming manual measurement in the study of sexual identities of prehistoric cave artists. Our study provides quantitative evidence relevant to sexual dimorphism and the sexual division of labor in Upper Paleolithic societies. In addition to analyzing historical handprint records, this method has potential applications in criminal forensics and human-computer interaction.

Original languageEnglish
Title of host publicationMM'10 - Proceedings of the ACM Multimedia 2010 International Conference
Pages1325-1332
Number of pages8
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10 - Firenze, Italy
Duration: 25 Oct 201029 Oct 2010

Other

Other18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10
CountryItaly
CityFirenze
Period25/10/1029/10/10

Fingerprint

Caves
Human computer interaction
Learning systems
Personnel

Keywords

  • archaeology
  • handprint
  • image analysis
  • prehistoric cave art
  • upper paleolithic

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

Cite this

Wang, J. Z., Ge, W., Snow, D. R., Mitra, P., & Giles, C. L. (2010). Determining the sexual identities of prehistoric cave artists using digitized handprints: A machine learning approach. In MM'10 - Proceedings of the ACM Multimedia 2010 International Conference (pp. 1325-1332) https://doi.org/10.1145/1873951.1874214

Determining the sexual identities of prehistoric cave artists using digitized handprints : A machine learning approach. / Wang, James Z.; Ge, Weina; Snow, Dean R.; Mitra, Prasenjit; Giles, C. Lee.

MM'10 - Proceedings of the ACM Multimedia 2010 International Conference. 2010. p. 1325-1332.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, JZ, Ge, W, Snow, DR, Mitra, P & Giles, CL 2010, Determining the sexual identities of prehistoric cave artists using digitized handprints: A machine learning approach. in MM'10 - Proceedings of the ACM Multimedia 2010 International Conference. pp. 1325-1332, 18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10, Firenze, Italy, 25/10/10. https://doi.org/10.1145/1873951.1874214
Wang JZ, Ge W, Snow DR, Mitra P, Giles CL. Determining the sexual identities of prehistoric cave artists using digitized handprints: A machine learning approach. In MM'10 - Proceedings of the ACM Multimedia 2010 International Conference. 2010. p. 1325-1332 https://doi.org/10.1145/1873951.1874214
Wang, James Z. ; Ge, Weina ; Snow, Dean R. ; Mitra, Prasenjit ; Giles, C. Lee. / Determining the sexual identities of prehistoric cave artists using digitized handprints : A machine learning approach. MM'10 - Proceedings of the ACM Multimedia 2010 International Conference. 2010. pp. 1325-1332
@inproceedings{1f832ec1778f4be9abc003f7907be443,
title = "Determining the sexual identities of prehistoric cave artists using digitized handprints: A machine learning approach",
abstract = "The sexual identities of human handprints inform hypotheses regarding the roles of males and females in prehistoric contexts. Sexual identity has previously been manually determined by measuring the ratios of the lengths of the individual's fingers as well as by using other physical features. Most conventional studies measure the lengths manually and thus are often constrained by the lack of scaling information on published images. We have created a method that determines sex by applying modern machine-learning techniques to relative measures obtained from images of human hands. This is the known attempt at substituting automated methods for time-consuming manual measurement in the study of sexual identities of prehistoric cave artists. Our study provides quantitative evidence relevant to sexual dimorphism and the sexual division of labor in Upper Paleolithic societies. In addition to analyzing historical handprint records, this method has potential applications in criminal forensics and human-computer interaction.",
keywords = "archaeology, handprint, image analysis, prehistoric cave art, upper paleolithic",
author = "Wang, {James Z.} and Weina Ge and Snow, {Dean R.} and Prasenjit Mitra and Giles, {C. Lee}",
year = "2010",
doi = "10.1145/1873951.1874214",
language = "English",
isbn = "9781605589336",
pages = "1325--1332",
booktitle = "MM'10 - Proceedings of the ACM Multimedia 2010 International Conference",

}

TY - GEN

T1 - Determining the sexual identities of prehistoric cave artists using digitized handprints

T2 - A machine learning approach

AU - Wang, James Z.

AU - Ge, Weina

AU - Snow, Dean R.

AU - Mitra, Prasenjit

AU - Giles, C. Lee

PY - 2010

Y1 - 2010

N2 - The sexual identities of human handprints inform hypotheses regarding the roles of males and females in prehistoric contexts. Sexual identity has previously been manually determined by measuring the ratios of the lengths of the individual's fingers as well as by using other physical features. Most conventional studies measure the lengths manually and thus are often constrained by the lack of scaling information on published images. We have created a method that determines sex by applying modern machine-learning techniques to relative measures obtained from images of human hands. This is the known attempt at substituting automated methods for time-consuming manual measurement in the study of sexual identities of prehistoric cave artists. Our study provides quantitative evidence relevant to sexual dimorphism and the sexual division of labor in Upper Paleolithic societies. In addition to analyzing historical handprint records, this method has potential applications in criminal forensics and human-computer interaction.

AB - The sexual identities of human handprints inform hypotheses regarding the roles of males and females in prehistoric contexts. Sexual identity has previously been manually determined by measuring the ratios of the lengths of the individual's fingers as well as by using other physical features. Most conventional studies measure the lengths manually and thus are often constrained by the lack of scaling information on published images. We have created a method that determines sex by applying modern machine-learning techniques to relative measures obtained from images of human hands. This is the known attempt at substituting automated methods for time-consuming manual measurement in the study of sexual identities of prehistoric cave artists. Our study provides quantitative evidence relevant to sexual dimorphism and the sexual division of labor in Upper Paleolithic societies. In addition to analyzing historical handprint records, this method has potential applications in criminal forensics and human-computer interaction.

KW - archaeology

KW - handprint

KW - image analysis

KW - prehistoric cave art

KW - upper paleolithic

UR - http://www.scopus.com/inward/record.url?scp=78650971537&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78650971537&partnerID=8YFLogxK

U2 - 10.1145/1873951.1874214

DO - 10.1145/1873951.1874214

M3 - Conference contribution

AN - SCOPUS:78650971537

SN - 9781605589336

SP - 1325

EP - 1332

BT - MM'10 - Proceedings of the ACM Multimedia 2010 International Conference

ER -