A Grassmann graph embedding framework for Gait analysis


Tee, Connie and Teoh, Andrew Ben Jin and Goh, Michael Kah Ong (2014) A Grassmann graph embedding framework for Gait analysis. EURASIP Journal on Advances in Signal Processing 2014, 2014 (15). ISSN 1687-6180

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Gait recognition is important in a wide range of monitoring and surveillance applications. Gait information has often been used as evidence when other biometrics is indiscernible in the surveillance footage. Building on recent advances of the subspace-based approaches, we consider the problem of gait recognition on the Grassmann manifold. We show that by embedding the manifold into reproducing kernel Hilbert space and applying the mechanics of graph embedding on such manifold, significant performance improvement can be obtained. In this work, the gait recognition problem is studied in a unified way applicable for both supervised and unsupervised configurations. Sparse representation is further incorporated in the learning mechanism to adaptively harness the local structure of the data. Experiments demonstrate that the proposed method can tolerate variations in appearance for gait identification effectively.

Item Type: Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 16 Jul 2014 04:24
Last Modified: 16 Jul 2014 04:24
URII: http://shdl.mmu.edu.my/id/eprint/5621


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