On covariate factor detection and removal for robust gait recognition

Tenika Whytock, Alexander Belyaev, Neil M. Robertson

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
156 Downloads (Pure)


We propose a novel bolt-on module capable of boosting the robustness of various single compact 2D gait representations. Gait recognition is negatively influenced by covariate factors including clothing and time which alter the natural gait appearance and motion. Contrary to traditional gait recognition, our bolt-on module remedies this by a dedicated covariate factor detection and removal procedure which we quantitatively and qualitatively evaluate. The fundamental concept of the bolt-on module is founded on exploiting the pixel-wise composition of covariate factors. Results demonstrate how our bolt-on module is a powerful component leading to significant improvements across gait representations and datasets yielding state-of-the-art results.
Original languageUndefined/Unknown
Pages (from-to)661-674
Number of pages14
JournalMachine Vision and Applications
Issue number5
Early online date18 Apr 2015
Publication statusPublished - Jul 2015

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