Extraction and Recognition of Nonlinear Interval-Type Features Using Symbolic KDA Algorithm with Application to Face Recognition

<table class="table-group" id="tab7"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr class="thead"><td align="left">Methods</td><td align="left">kernel Fisherfaces</td><td align="left">kernel Eigenfaces</td><td align="left">symbolic KDA</td></tr><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr><td align="left">Recognition rates (%)</td><td align="left">87.15</td><td align="left">83.28</td><td align="left">89.00</td></tr><tr><td align="left">Training time (seconds)</td><td align="left">48.346</td><td align="left">68.751</td><td align="left">38.324</td></tr><tr><td align="left">Feature dimension</td><td align="left">42</td><td align="left">54</td><td align="left">15</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Comparison of classification performance
using Yale face database.

Journal of Electrical and Computer Engineering

tab7

Table 3

Table 3: Extraction and Recognition of Nonlinear Interval-Type Features Using Symbolic KDA Algorithm with Application to Face Recognition 