Boosting Discriminant Learners for Gait Recognition Using MPCA Features

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 a 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 shows that the proposed solution has successfully improved the gait recognition performance and outperformed several state-of-theart gait recognition algorithms.