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

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

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

6 Scopus citations

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 (US)
Title of host publicationMM'10 - Proceedings of the ACM Multimedia 2010 International Conference
Pages1325-1332
Number of pages8
DOIs
StatePublished - Dec 1 2010
Event18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10 - Firenze, Italy
Duration: Oct 25 2010Oct 29 2010

Publication series

NameMM'10 - Proceedings of the ACM Multimedia 2010 International Conference

Other

Other18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10
CountryItaly
CityFirenze
Period10/25/1010/29/10

All Science Journal Classification (ASJC) codes

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

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    Wang, J., 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). (MM'10 - Proceedings of the ACM Multimedia 2010 International Conference). https://doi.org/10.1145/1873951.1874214