báo cáo hóa học:" Research Article Boosting Discriminant Learners for Gait Recognition Using MPCA Features"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài:Research Article Boosting Discriminant Learners for Gait Recognition Using MPCA Features | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2009 Article ID713183 11 pages doi 2009 713183 Research Article Boosting Discriminant Learners for Gait Recognition Using MPCA Features Haiping Lu 1 K. N. Plataniotis 2 and A. N. Venetsanopoulos3 1 The Institute for Infocomm Research Agency for Science Technology and Research Singapore 138632 2 The Edwards. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada M5S3G4 3 Department of Electrical and Computer Engineering Ryerson University Toronto ON Canada M5B 2K3 Correspondence should be addressed to Haiping Lu hplu@ Received 24 January 2009 Revised 6 June 2009 Accepted 9 July 2009 Recommended by Yoichi Sato This paper proposes a boosted linear discriminant analysis LDA solution on features extracted by the multilinear principal component analysis MPCA to enhance gait recognition performance. Three-dimensional gait objects are projected in the MPCA space first to obtain low-dimensional tensorial features. Then lower-dimensional vectorial features are obtained through discriminative feature selection. These feature vectors are then fed into an LDA-style booster where several regularized and weakened LDA learners work together to produce a strong learner through a novel feature weighting and sampling process. The LDA learner employs a simple nearest-neighbor classifier with a weighted angle distance measure for classification. The experimental results on the NIST USF Gait Challenge data-sets show that the proposed solution has successfully improved the gait recognition performance and outperformed several state-of-the-art gait recognition algorithms. Copyright 2009 Haiping Lu et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. Introduction Automated human .

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