On Reducing the effect of Covariate Factors in Gait Recognition: A Classifier Ensemble Method
- Submitting institution
-
University of Newcastle upon Tyne
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 238136-199833-1292
- Type
- D - Journal article
- DOI
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10.1109/TPAMI.2014.2366766
- Title of journal
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Article number
- -
- First page
- 1521
- Volume
- 37
- Issue
- 7
- ISSN
- 0162-8828
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2014
- URL
-
https://doi.org/10.1109/TPAMI.2014.2366766
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
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C - Open Lab
- Citation count
- 66
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper re-examined the challenges in video-based gait recognition via CCTV cameras, and proposed a feasible machine learning solution that is robust to many covariate factors such as clothing and camera angle. The results of this work have substantially influenced other machine learning applications including source device identification, palmprint identification, and object recognition (with attribute learning). This work led to an H2020 award: “Computer Vision Enabled Multimedia Forensics and People Identification” (2M Euro).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -