Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms

<table class="table-group" id="tab9"><tr><td><table class="table"><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr class="thead"><td class="align_left">Group</td><td class="align_center">Prestance</td><td class="align_center">Midstance</td><td class="align_center">Terminal stance</td><td class="align_center">Preswing</td></tr><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr><td class="align_left">1</td><td class="align_center">90.91</td><td class="align_center">98.05</td><td class="align_center">99.07</td><td class="align_center">99.03</td></tr><tr><td class="align_left">2</td><td class="align_center">91.07</td><td class="align_center">97.67</td><td class="align_center">98.26</td><td class="align_center">98.93</td></tr><tr class="table-tr"><td colspan="5"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Recognition results of multiple feature combination with SVM (%).</div>

Computational Intelligence and Neuroscience

tab9

Table 9

Table 9: Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms 