# View-invariant Gait Recognition through Genetic Template Segmentation

## TL;DR

This paper introduces a genetic algorithm-based method for automating the segmentation of gait templates, improving view-invariant gait recognition performance by effectively isolating key body regions.

## Contribution

The proposed genetic template segmentation (GTS) automates boundary selection in gait templates, enhancing recognition accuracy over manual methods.

## Key findings

- GEI template segmentation yields the best results.
- GTS significantly outperforms existing view-invariant gait recognition methods.
- Automated segmentation improves robustness against covariates.

## Abstract

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.