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Comparing machine learning classifiers and linear/logistic regression to explore the relationship between hand dimensions and demographic characteristics

Research output: Contribution to journalJournal article

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  • Oscar Miguel-Hurtado
  • Richard Guest
  • Sarah V. Stevenage
  • Greg J. Neil
  • Sue Black
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Article numbere0165521
<mark>Journal publication date</mark>2/11/2016
<mark>Journal</mark>PLoS ONE
Issue number11
Volume11
Number of pages25
Publication statusPublished
Original languageEnglish

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.