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Pose-based gait recognition with local gradient descriptors and hierarchically aggregated residuals

[+] Author Affiliations
Dimitris Kastaniotis, Ilias Theodorakopoulos, Spiros Fotopoulos

University of Patras, Department of Physics, University Campus, Rion 26504, Greece

J. Electron. Imaging. 25(6), 063019 (Dec 14, 2016). doi:10.1117/1.JEI.25.6.063019
History: Received July 2, 2016; Accepted November 17, 2016
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Abstract.  We focus on the problem of pose-based gait recognition. Our contribution is two-fold. First, we incorporate a local histogram descriptor that allows us to encode the trajectories of selected limbs via a one-dimensional version of histogram of oriented gradients features. In this way, a gait sequence is encoded into a sequence of local gradient descriptors. Second, we utilize a robust encoding method in which the residuals of local descriptors, with respect to a discriminative model, are aggregated into fixed length vectors. This technique combines the advantages of both residual aggregation and soft-assignment techniques, resulting in a powerful vector representation. For classification purposes, we use a nonlinear kernel to map vectors into a reproducing kernel Hilbert space. Then, we classify an encoded gait sequence according to the sparse representation-based classification method. Experimental evaluation on two publicly available datasets demonstrates the effectiveness of the proposed scheme on both recognition and verification tasks.

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Citation

Dimitris Kastaniotis ; Ilias Theodorakopoulos and Spiros Fotopoulos
"Pose-based gait recognition with local gradient descriptors and hierarchically aggregated residuals", J. Electron. Imaging. 25(6), 063019 (Dec 14, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.6.063019


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