Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

• Published in: PloS one Vol. 11; no. 11; p. e0165521
• Main Authors: Miguel-Hurtado, Oscar, Guest, Richard, Stevenage, Sarah V., Neil, Greg J., Black, Sue
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.11.2016; Public Library of Science (PLoS)
• Subjects: 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
• DOI: 10.1371/journal.pone.0165521

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.