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Random subspace method for gait recognition
conference contribution
posted on 2012-08-16, 00:00 authored by Yu Guan, Chang-Tsun LiChang-Tsun Li, Yongjian HuOver fitting is a common problem for gait recognition algorithms when gait sequences in gallery for training are acquired under a single walking condition. In this paper, we propose an approach based on the random subspace method (RSM) to address such over learning problems. Initially, two-dimensional Principle Component Analysis (2DPCA) is adopted to obtain the full hypothesis space (i.e., eigen space). Multiple inductive biases (i.e., subspaces) are constructed, each with the corresponding basis vectors randomly chosen from the initial eigen space. This procedure can not only largely avoid over adaptation but also facilitate dimension reduction. The final classification is achieved by the decision committee which follows a majority voting criterion from the labeling results of all the subspaces. Experimental results on the benchmark USF Human ID gait database show that the proposed method is a feasible framework for gait recognition under unknown walking conditions.
History
Event
Multimedia and Expo Workshops. IEEE International Conference (2012 : Melbourne, Victoria)Pagination
284 - 289Publisher
Institute of Electrical and Electronics Engineers (IEEE)Location
Melbourne, VictoriaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2012-07-09End date
2012-07-13ISSN
2330-7927ISBN-13
9780769547299Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2012, IEEETitle of proceedings
Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012Usage metrics
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