Windowed DMD for Gait Recognition Under Clothing and Carrying Condition Variations

In this paper, we introduce a method based on Windowed Dynamic Mode Decomposition to enhance the texture of body parts on the Gait Energy Image that are not affected by the clothing and carrying condition variations, in order to improve the gait recognition accuracy under these kinds of variations. We obtain the best accurracy (\(71.37 \%\)) for large carrying condition variations reported in the literature for CASIA-B dataset. Unlike the deep learning based approaches the proposal method is simple and does not need training.

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