## 3 Results
### Analysis of Verification Results of Different Data  
#### 3.5.1 Continuous Occlusion Results for the Extended Yale B Database  
The Extended Yale B [23] face database is comprised of 64 face images per object that contain different lighting conditions (see Figure 12). There are a total of 38 objects, of which the 11 th and 13 th objects have only 60 images. The 12 th contains 59, the 15 th has 62, and the 14 th, 16 th, and 17 th have 63. Therefore, in the experiment, the first 26 images of each object were selected as training samples, and the last 33 images were randomly occluded as test samples. A total of 38 objects were selected. Each image was downsampled from the original 165 \(\times\) 120 pixels to 15 \(\times\) 10 pixels, 20 \(\times\) 15 pixels, and 25 \(\times\) 20 pixels for the analysis. Figure 13 shows occlusion maps of different proportions that were used with the test samples. The results of Extended Yale B database recognition are shown in Tables 4 and 5. The results in the table show that the proposed BPLRC method was significantly better than the LRC, SRC, CRC, ESRC, and Module LRC (five-block processing) algorithms. For 25 \(\times\) 20 pixels images, the average time taken for the BPLRC algorithm to identify each image was 0.08 s (see Table 6), and the calculation time was not very long. However, lengthening the calculation time of the LRC algorithm is sometimes necessary in exchange for higher robustness. For example, in Table 4, the higher the occlusion rate of the test image, the greater the difference between the accuracy of the BPLRC and LRC methods. Figure 12: Some face images from Extended Yale B [Reprinted with permission from Elsevier [20]. Copyright 2013, Neurocomputing]. Figure 13: Occlusion maps of different proportions used with the test samples: (**a**) face image with an occlusion ratio of 10%; (**b**) face image with an occlusion ratio of 20%; (**c**) face image with an occlusion ratio of 30%; (**d**) face image with an occlusion ratio of 40% [Reprinted with permission from Elsevier [20]. Copyright 2013, Neurocomputing]. However, randomly generating occlusion blocks to occlude the faces in the Extended Yale B dataset needs to be more comprehensive. The two special cases of vertical and diagonal occlusion also need to be considered. The vertical occlusion of 10% of faces is shown in Figure 14a,b, and the diagonal occlusion of 20% of faces is shown in Figure 14c,d. It can be seen from Table 5 that, compared to the recognition accuracy of BPLRC when images are randomly occluded by 10%, in most cases, the recognition accuracy of BPLRC is higher than that of LRC, SRC, CRC, ESRC, and Module LRC. For example, in the case of 20% diagonal occlusion, the SRC method has better recognition accuracy than BPLRC for images downsampled to 15 \(\times\) 10 pixels. The biggest reason is the particularity of the occlusion distribution and the small size of the image after downsampling. The results in Table 5 show that, with the diagonal face occlusion in this special case, good results can also be obtained when using the BPLRC method. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Image Size**} & \multicolumn{6}{c}{**Method**} \\ \cline{2-7}  & **LRC** & **SRC** & **CRC** & **ESRC** & **Module LRC** & **BPLRC** \\ \hline \(15\times 10\) & **0.83** & 248.20 & 1.40 & 255.64 & 6.89 & 45.69 \\ \(20\times 15\) & **0.9** & 770.95 & 1.59 & 860.85 & 12.67 & 72.87 \\ \(25\times 20\) & **1.04** & 2231.74 & 2.03 & 2073.83 & 15.08 & 100.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Computational time (seconds) for different methods to identify 1254 images.