Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

Miguel-Hurtado, Oscar and Guest, Richard and Stevenage, Sarah V. and Neil, Greg J. and Black, Sue (2016) Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics. PLOS ONE, 11 (11). e0165521. ISSN 1932-6203. (doi:https://doi.org/10.1371/journal.pone.0165521) (Full text available)

PDF (Gold Open Access) - Publisher pdf

Creative Commons Licence
This work is licensed under a Creative Commons Attribution 4.0 International License.
Download (3MB) Preview
[img]
Preview
Official URL
http://doi.org/10.1371/journal.pone.0165521

Abstract

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications.

Item Type: Article
Subjects: T Technology
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Tina Thompson
Date Deposited: 03 Nov 2016 09:23 UTC
Last Modified: 08 Nov 2016 11:41 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/58352 (The current URI for this page, for reference purposes)
  • Depositors only (login required):

Downloads

Downloads per month over past year