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, employs the genetic algorithm to automate the boundary selection process. This method was tested on the gait energy image (GEI), gait entropy image, and active energy image 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.