1 research outputs found
View-invariant Gait Recognition through Genetic Template Segmentation
Template-based model-free approach provides by far the most successful
solution to the gait recognition problem in literature. Recent work discusses
how isolating the head and leg portion of the template increase the performance
of a gait recognition system making it robust against covariates like clothing
and carrying conditions. However, most involve a manual definition of the
boundaries. The method we propose, the genetic template segmentation (GTS),
employs the genetic algorithm to automate the boundary selection process. This
method was tested on the GEI, GEnI and AEI templates. GEI seems to exhibit the
best result when segmented with our approach. Experimental results depict that
our approach significantly outperforms the existing implementations of
view-invariant gait recognition.Comment: Published in IEEE Signal Processing Letters. Received April 24, 2017,
revised June 6, 2017, accepted June 10, 2017, published June 14, 201