Abstract
The extraction of discriminative features in presence of covariates for robust human gait recognition is a challenging task. The effect of covariate can be modelled as unknown feature contamination problem resulting in the conversion of useful or relevant feature into irrelevant one. This study presents new gait representation and recognition technique. The new technique represents gait features based on the Gabor function and discrete cosine transform of binary silhouettes. It is called as Gabor cosine feature, which represents binary gait video sequence as third order tensor. The discrimination capability of the extracted gait features has been enhanced using a new multilinear Laplacian discriminant analysis (MLDA). MLDA exploits benefit of Laplacian weighted scatter difference instead of simple scatter difference, generalised Rayleigh quotient as a class separability measure. The feasibility and performance of the proposed scheme has been evaluated using USF, CASIA, OU‐ISIR dataset. The experimental results show competitive performance in comparison with conventional gait recognition schemes.
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