title: Comparative analysis and fusion of spatiotemporal information for footstep recognition.
abstract: Footstep recognition is a relatively new biometric which aims to discriminate people using walking characteristics extracted from floor-based sensors. This paper reports for the first time a comparative assessment of the spatiotemporal information contained in the footstep signals for person recognition. Experiments are carried out on the largest footstep database collected to date, with almost 20,000 valid footstep signals and more than 120 people. Results show very similar performance for both spatial and temporal approaches (5 to 15 percent EER depending on the experimental setup), and a significant improvement is achieved for their fusion (2.5 to 10 percent EER). The assessment protocol is focused on the influence of the quantity of data used in the reference models, which serves to simulate conditions of different potential applications such as smart homes or security access scenarios.
authors.affiliation: Biometric Recognition Group—ATVS, Universidad Autonoma de Madrid, C\Francisco Tomas y Valiente 11, EPS, Madrid 28049, Spain. ruben.vera@uam.es
authors.forename: Ruben
authors.initials: R
authors.lastname: Vera-Rodriguez
authors.forename: John S D
authors.initials: JS
authors.lastname: Mason
authors.forename: Julian
authors.initials: J
authors.lastname: Fierrez
authors.forename: Javier
authors.initials: J
authors.lastname: Ortega-Garcia
mesh_terms: D001696:Biomechanical Phenomena / Q000502:physiology; D056667:Biometric Identification / Q000295:instrumentation / Q000379:methods*; D005528:Foot / Q000502:physiology*; D005684:Gait / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D011312:Pressure; D012815:Signal Processing, Computer-Assisted; D014741:Video Recording; D016138:Walking