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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| Published in | PloS one Vol. 11; no. 11; p. e0165521 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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02.11.2016
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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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Miguel-Hurtado, Oscar Black, Sue Guest, Richard Neil, Greg J. Stevenage, Sarah V. |
| AuthorAffiliation | National University of Defense Technology, CHINA 2 Department of Psychology, University of Southampton, Southampton, United Kingdom 1 School of Engineering and Digital Arts, University of Kent, Canterbury, United Kingdom 3 Centre for Anatomy and Human Identification, University of Dundee, Dundee, United Kingdom |
| AuthorAffiliation_xml | – name: 3 Centre for Anatomy and Human Identification, University of Dundee, Dundee, United Kingdom – name: National University of Defense Technology, CHINA – name: 2 Department of Psychology, University of Southampton, Southampton, United Kingdom – name: 1 School of Engineering and Digital Arts, University of Kent, Canterbury, United Kingdom |
| Author_xml | – sequence: 1 givenname: Oscar orcidid: 0000-0002-3257-5170 surname: Miguel-Hurtado fullname: Miguel-Hurtado, Oscar – sequence: 2 givenname: Richard surname: Guest fullname: Guest, Richard – sequence: 3 givenname: Sarah V. surname: Stevenage fullname: Stevenage, Sarah V. – sequence: 4 givenname: Greg J. surname: Neil fullname: Neil, Greg J. – sequence: 5 givenname: Sue surname: Black fullname: Black, Sue |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27806075$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2016 Public Library of Science 2016 Miguel-Hurtado et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2016 Miguel-Hurtado et al 2016 Miguel-Hurtado et al |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 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. |
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| Title | Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics |
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