期刊
PLOS ONE
卷 11, 期 11, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0165521
关键词
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资金
- UK Engineering and Physical Sciences Research Council, SID: An Exploration of Super-Identity project (EPSRC) [EP/J004995/1]
- Engineering and Physical Sciences Research Council [EP/J004995/1] Funding Source: researchfish
- EPSRC [EP/J004995/1] Funding Source: UKRI
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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