Journal
EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING
Volume -, Issue -, Pages -Publisher
SPRINGER
DOI: 10.1155/2009/713183
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Funding
- Ontario Centres of Excellence
- Bell University Laboratories at the University of Toronto
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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 (C) 2009 Haiping Lu et al.
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