Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Comparing machine learning classifiers and linear/logistic regression to explore the relationship between hand dimensions and demographic characteristics. / Miguel-Hurtado, Oscar; Guest, Richard; Stevenage, Sarah V. et al.
In: PLoS ONE, Vol. 11, No. 11, e0165521, 02.11.2016.Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Comparing machine learning classifiers and linear/logistic regression to explore the relationship between hand dimensions and demographic characteristics
AU - Miguel-Hurtado, Oscar
AU - Guest, Richard
AU - Stevenage, Sarah V.
AU - Neil, Greg J.
AU - Black, Sue
PY - 2016/11/2
Y1 - 2016/11/2
N2 - 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.
AB - 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.
U2 - 10.1371/journal.pone.0165521
DO - 10.1371/journal.pone.0165521
M3 - Journal article
VL - 11
JO - PLoS ONE
JF - PLoS ONE
SN - 1932-6203
IS - 11
M1 - e0165521
ER -