Boosting discriminant learners for gait recognition using MPCA features

Authors:
Haiping Lu;K. N. Plataniotis;A. N. Venetsanopoulos
Affiliations:
The Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore;The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada;Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada
Venue:
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
Year:
2009

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Abstract

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.