access icon free Multilinear Laplacian discriminant analysis for gait recognition

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.

Inspec keywords: discrete cosine transforms; image sequences; Laplace transforms; feature extraction; tensors; gait analysis; image recognition

Other keywords: MLDA; Gabor function; robust human gait recognition; gait feature extraction; unknown feature contamination problem; Laplacian weighted scatter difference; binary silhouettes; third order tensor; CASIA dataset; binary gait video sequence; USF dataset; gait feature representation; generalised Rayleigh quotient; class separability measure; Gabor cosine feature; OU-ISIR dataset; discrete cosine transform; multilinear Laplacian discriminant analysis

Subjects: Integral transforms; Algebra; Computer vision and image processing techniques; Integral transforms; Image recognition; Algebra

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