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 asses...

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Vydané v:PloS one Ročník 11; číslo 11; s. e0165521
Hlavní autori: Miguel-Hurtado, Oscar, Guest, Richard, Stevenage, Sarah V., Neil, Greg J., Black, Sue
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 02.11.2016
Public Library of Science (PLoS)
Predmet:
Access control
Algorithms
Artificial intelligence
Automation
Biology and Life Sciences
Biometrics
Classifiers
Computer and Information Sciences
Datasets
Demographics
Demography
Demography - methods
Engineering schools
Female
Forensic engineering
Forensic science
Hand - anatomy & histology
Humans
Identification
International conferences
Learning
Learning algorithms
Legal medicine
Linear Models
Logistic Models
Machine Learning
Male
Medicine and Health Sciences
People and Places
Physical Sciences
Physiology
Regression
Research and Analysis Methods
United Kingdom > UK
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí: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.
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Competing Interests: The authors have declared that no competing interests exist.
Data curation: OM RG SS GN SB. Formal analysis: OM RG SS GN SB. Funding acquisition: RG SS SB. Investigation: OM RG SS GN SB. Methodology: OM RG SS GN SB. Project administration: OM RG SS GN SB. Resources: OM RG SS GN SB. Software: OM RG SS GN SB. Supervision: OM RG SS GN SB. Validation: OM RG SS GN SB. Visualization: OM RG SS GN SB. Writing – original draft: OM RG SS GN SB. Writing – review & editing: OM RG SS GN SB.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0165521