Patch Based Collaborative Representation with Gabor Feature and Measurement Matrix for Face Recognition

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/mpe/volume-2018/3025264/figures/3025264.fig.005a.svgz" name="3025264.fig.005a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>10 training samples</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/mpe/volume-2018/3025264/figures/3025264.fig.005b.svgz" name="3025264.fig.005b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>20 training samples</td></tr></table>

<div>The performance of each dimension reduction method for GPCRC on Extended Yale B database.</div>

Mathematical Problems in Engineering

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Figure 5

Figure 5: Patch Based Collaborative Representation with Gabor Feature and Measurement Matrix for Face Recognition 