Boosting Discriminant Learners for Gait Recognition Using MPCA Features

EURASIP Journal on Image and Video Processing. 2009;2009 DOI 10.1155/2009/713183


Journal Homepage

Journal Title: EURASIP Journal on Image and Video Processing

ISSN: 1687-5176 (Print); 1687-5281 (Online)

Publisher: SpringerOpen

Society/Institution: European Association for Signal Processing (EURASIP)

LCC Subject Category: Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML



Haiping Lu
K. N. Plataniotis
A. N. Venetsanopoulos


Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 13 weeks


Abstract | Full Text

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.