Improving the Component-Based Face Recognition Using Enhanced Viola–Jones and Weighted Voting Technique

<table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr class="thead"><td class="align_left">Face recognition method</td><td class="align_center">2</td><td class="align_center">3</td><td class="align_center">4</td><td class="align_center">5</td></tr><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr><td class="align_left">Average KNN success rate</td><td class="align_center">0.92</td><td class="align_center">0.93</td><td class="align_center">0.95</td><td class="align_center">0.97</td></tr><tr><td class="align_left">Facial component priority voting</td><td class="align_center">0.97</td><td class="align_center">0.98</td><td class="align_center">0.99</td><td class="align_center">1</td></tr><tr class="table-tr"><td colspan="5"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Recognition success rates improvement using our approach (PUT database).</div>

Modelling and Simulation in Engineering

tab3

Table 3

Table 3: Improving the Component-Based Face Recognition Using Enhanced Viola–Jones and Weighted Voting Technique 