Human footsteps can provide a unique behavioural pattern for robust biometric systems. Traditionally, security systems have been based on passwords or security access cards. Biometric recognition deals with the design of security systems for automatic identification or verification of a human subject (client) based on physical and behavioural characteristics. In this talk, I will present spatio-temporal raw and processed footstep data representations designed and evaluated on deep machine learning models based on a two-stream resnet architecture, by using the SFootBD database the largest footstep database to date with more than 120 people and almost 20,000 footstep signals. 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. 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). In these scenarios we report state-of-the-art footstep recognition rates.
Biography: 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 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 and machine learning. He is currently a PhD candidate in Electrical and Electronics Engineering at the University of Manchester, U.K. He has published papers on deep machine learning for gait analysis in security and healthcare. He received the Best Student Paper Award in Optical Sensing applications at the 2015 IEEE Sensors Conference.