14 December 2016 Pose-based gait recognition with local gradient descriptors and hierarchically aggregated residuals
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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.
Dimitris Kastaniotis, Dimitris Kastaniotis, Ilias Theodorakopoulos, Ilias Theodorakopoulos, Spiros Fotopoulos, Spiros Fotopoulos, } "Pose-based gait recognition with local gradient descriptors and hierarchically aggregated residuals," Journal of Electronic Imaging 25(6), 063019 (14 December 2016). https://doi.org/10.1117/1.JEI.25.6.063019 . Submission:


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