SCI of coding vectors under different levels of corruptions.

<p>SCI of coding vectors under different levels of corruptions.</p

Recognition rates for test samples with different level of random corruption.

<p>(A) 10% random corruption (B) 20% random corruption. (C) 30% random corruption (D) 40% random corruption.</p

Comparison of mean CPU time(measured in second) on the Extended Yale B database.

<p>Comparison of mean CPU time(measured in second) on the Extended Yale B database.</p

Comparison of CPU time on the AR database.

<p>Comparison of CPU time on the AR database.</p

Recognition accuracy on the FERET database.

<p>Recognition accuracy on the FERET database.</p

Demonstration of the idea of the modular SRC-KNS.

<p>The test sample (from class 1) with scarf occlusion is divided into 8 blocks. The distances of the fourth block are shown in the top right while the distances of the eighth block are shown in the bottom right. The dotted lines indicate the mean distance for that block. is significantly bigger than . Therefore, using blocks with bigger can correctly identify the test sample, while the blocks with smaller are not helpful for classification in this case. The subject of the photograph has given written informed consent, as outlined in the PLOS consent form, to publication of their photograph.</p

Comparison of CPU time on the FERET database.

<p>Comparison of CPU time on the FERET database.</p

Recognition accuracy on the AR database.

<p>Recognition accuracy on the AR database.</p

Comparison of mean CPU time on the Extended Yale B database with different corruption levels.

<p>Comparison of mean CPU time on the Extended Yale B database with different corruption levels.</p

Recognition rates for occluded cases. (A) sunglass occlusion. (B) scarf occlusion.

<p>Recognition rates for occluded cases. (A) sunglass occlusion. (B) scarf occlusion.</p