A robust speed-invariant gait recognition system for walker and runner identification

Yu Guan, Chang Tsun Li

Research output: Book chapter/Published conference paperConference paper

47 Citations (Scopus)


In real-world scenarios, walking/running speed is one of the most common covariate factors that can affect the performance of gait recognition systems. By assuming the effect caused by the speed changes (from the query walker-s/runners) are intra-class variations that the training data (i.e., gallery) fails to capture, overfitting to the less representative training data may be the main problem that degrades the performance. In this work, we employ a general model based on random subspace method to solve this problem. More specifically, for query gaits in unknown speeds, we try to reduce the generalization errors by combining a large number of weak classifiers. We evaluate our method on two benchmark databases, i.e., Infrared CASIA-C dataset and Treadmill OU-ISIR-A dataset. For the cross-speed walking/running gait recognition experiments, nearly perfect results are achieved, significantly higher than other state-of-the-art algorithms. We also study the unknown- speed nrunner identification solely using the walking gait gallery, and the encouraging experimental results suggest the effectiveness of our method in such cross-mode gait recognition tasks.
Original languageEnglish
Title of host publicationProceedings - 2013 International Conference on Biometrics, ICB 2013
Subtitle of host publicationICB 2013
EditorsJulian Fierrez, Ajay Kumar, Mayank Vatsa, Raymond Veldhuis, Javier Ortega-Garcia
Place of PublicationUnited States
PublisherIEEE Computer Society
Number of pages8
ISBN (Electronic)978-147990310-8
ISBN (Print)9781479903108
Publication statusPublished - 2013
EventThe 6th IAPR International Conference on Biometrics: ICB-2013 - Madrid Higher Technical School of Mining Engineering, Madrid, Spain
Duration: 04 Jun 201307 Jun 2013


ConferenceThe 6th IAPR International Conference on Biometrics
Internet address

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