RESUMEN
Forensic evidence often relies on a combination of accurately recorded measurements, estimated measurements from landmark data such as a subject's stature given a known measurement within an image, and inferred data. In this study a novel dataset is used to explore linkages between hand measurements, stature, leg length and stride. These three measurements replicate the type of evidence found in surveillance videos with stride being extracted from an automated gait analysis system. Through correlations and regression modelling, it is possible to generate accurate predictions of stature from hand size, leg length and stride length (and vice versa), and to predict leg and stride length from hand size with, or without, stature as an intermediary variable. The study also shows improved accuracy when a subject's sex is known a-priori. Our method and models indicate the possibility of calculating or checking relationships between a suspect's physical measurements, particularly when only one component is captured as an accurately recorded measurement.
Asunto(s)
Identificación Biométrica/métodos , Estatura , Marcha , Mano/anatomía & histología , Velocidad al Caminar , Femenino , Ciencias Forenses , Humanos , Procesamiento de Imagen Asistido por Computador , Modelos Logísticos , Masculino , Grabación en VideoRESUMEN
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.