Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 35; no. 4; pp. 823 - 834
• Main Authors: Vera-Rodriguez, R., Mason, J. S. D., Fierrez, J., Ortega-Garcia, J.
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.04.2013; IEEE Computer Society
• Subjects: Applied sciences; Artificial intelligence; Biomechanical Phenomena - physiology; Biometric Identification - instrumentation; Biometric Identification - methods; Biometrics; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Exact sciences and technology; Feature extraction; Foot - physiology; footstep recognition; Gait - physiology; gait recognition; Humans; Intelligent sensors; Legged locomotion; Models, Biological; pattern recognition; Pattern recognition. Digital image processing. Computational geometry; Pressure; pressure analysis; Sensor fusion; Sensor phenomena and characterization; Signal Processing, Computer-Assisted; Software; Walking; Biometrics; Space time correlation; Legged locomotion; Computer vision; Home automation; Image recognition; gait recognition; pressure analysis; Gait; Pattern recognition; Posture; Walking; Reference model; Data fusion; footstep recognition
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2012.164

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.