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). ISSN 1932-6203

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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:
Journal Article
Journal or Publication Title:
PLoS ONE
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2700
Subjects:
ID Code:
132592
Deposited By:
Deposited On:
11 Apr 2019 14:00
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Nov 2020 07:23