Analysis of spatio-temporal representations for robust footstep recognition with deep residual neural networks
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
10.1109/TPAMI.2018.2799847IEEE Transactions on Pattern Analysis and Machine Intelligence, 30 January (2018): 1-13
ISSN0162-8828 (print); 1939-3539 (online)
Funded byThis work has been partially supported by Cognimetrics TEC2015-70627-R MINECO/FEDER
ProjectGobierno de España. TEC2015-70627-R
SubjectsBiological system modeling; Biometric system; Biometrics (access control); Data models; Databases; Deep learning; Floor sensor system; Footstep recognition; Hidden Markov models; Security; Sensor systems; Verification system; Informática
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Rights© 2018 IEEE
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 to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering 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 made available for model training: at airports security checkpoints (smallest training set), workspace environments (medium training set) and home environments (largest training set). We report state-of-the-art footstep recognition rates with an optimal equal false acceptance and false rejection rate of 0.7% (equal error rate), an improvement ratio of 371% from previous state-of-the-art. We perform a feature analysis of deep residual neural networks showing effective clustering of client&#x0027;s footstep data and provide insights of the feature learning process.
Google Scholar:Costilla Reyes, Omar - Vera Rodríguez, Rubén - Scully, Patricia - Ozanyan, Krikor B.
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