Analysis of Spatio-temporal Representations for Robust Footstep Recognition with Deep Residual Neural Networks

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• Omar Costilla - Reyes (University of Manchester)
• Thursday 25 January 2018, 15:00-16:00
• FW26, Computer Laboratory, William Gates Building.

If you have a question about this talk, please contact Liang Wang.

Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database contains a large cohort of impostors and a small set of clients to verify the reliability of biometric systems. We provide experimental results in 3 critical data-driven security scenarios, according to the amount of footstep data available for model training: at airports security checkpoints (smallest training set), workspace environments (medium training set) and home environments (largest training set). With this methodology, we report state-of-the-art footstep recognition rates.

Bio: Omar Costilla-Reyes Received the M.Sc. degree in Electrical Engineering from the University of North Texas, Texas, U.S.A. in 2014. During his master studies, he was a research assistant in projects with funding from the National Science Foundation (NSF) and National Aeronautics and Space Administration (NASA). His M.Sc. dissertation was on dynamic indoor positioning systems using wireless sensor networks. He is currently a research associate and PhD candidate in Electrical and Electronics Engineering at the University of Manchester, U.K. He has published papers on applications of machine learning for gait analysis in security and healthcare. His research interest lies in applications of machine learning using sensor systems for security and healthcare. He received the Best Student Paper Award in Optical Sensing applications at the 2015 IEEE Sensors Conference. His Journal paper entitled “Temporal Pattern Recognition in Gait Activities Recorded With a Footprint Imaging Sensor System” was one of the 25 most downloaded IEEE Sensors Journal papers from January to June 2017. He has won scholarships and awards for academic achievement including an academic scholarship for his master’s and doctorate studies from the Mexican Science council (CONACyT).

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