Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics.
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.
School of Engineering and Digital Arts, University of Kent, Canterbury, United Kingdom.
Oscar
http://orcid.org/0000-0002-3257-5170
O
Miguel-Hurtado
School of Engineering and Digital Arts, University of Kent, Canterbury, United Kingdom.
Richard
R
Guest
Department of Psychology, University of Southampton, Southampton, United Kingdom.
Sarah V
SV
Stevenage
Department of Psychology, University of Southampton, Southampton, United Kingdom.
Greg J
GJ
Neil
Centre for Anatomy and Human Identification, University of Dundee, Dundee, United Kingdom.
Sue
S
Black
D000465:Algorithms; D003710:Demography / Q000379:methods*; D005260:Female; D006225:Hand / Q000033:anatomy & histology*; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male