Robust gait recognition from extremely low frame-rate videos

Yu Guan, Chang Tsun Li, Sruti Das Choudhury

Research output: Book chapter/Published conference paperConference paperpeer-review

12 Citations (Scopus)


In this paper, we propose a gait recognition method for extremely low frame-rate videos. Different from the popular temporal reconstruction-based methods, the proposed method uses the average gait over the whole sequence as input feature template. Assuming the effect caused by extremely low frame-rate or large gait fluctuations are intra-class variations that the gallery data fails to capture, we build a general model based on random subspace method. More specifically, a number of weak classifiers are combined to reduce the generalization errors. We evaluate our method on the OU-ISIR-D dataset with large/small gait fluctuations, and very competitive results are achieved when both the probe and gallery are extremely low frame-rate gait sequences (e.g., 1 fps).
Original languageEnglish
Title of host publication2013 International Workshop on Biometrics and Forensics (IWBF 2013)
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Print)9781467349895
Publication statusPublished - 2013
Event1st International Workshop on Biometrics and Forensics: IWBF 2013 - Instituto Superior Técnico Congress Center, Lisbon, Portugal
Duration: 04 Apr 201305 Apr 2013


Conference1st International Workshop on Biometrics and Forensics
Abbreviated titleIntegrating Biometrics and Forensics for the Digital Age
Internet address

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