## 3 Results
### Analysis of Verification Results of Different Data
#### 3.5.2 Continuous Occlusion Results with the ORL Database  
There were 10 grayscale images per object in the ORL [24] face database for a total of 40 objects. Some of these images were different in terms of shooting time, lighting, facial expressions (eyes open/closed, smiling), and facial details (glasses). All images in the ORL database were selected for the experiment. The first six images were used as training samples. The last four images were randomly occluded as test samples (see Figure 15). The pixel size of each image was downsampled from the original 112 \(\times\) 92 pixels to 15 \(\times\) 10 pixels, 20 \(\times\) 15 pixels, and 25 \(\times\) 20 pixels for the experiments. The processing method for the ORL database was consistent with the Extended Yale B database. The results of ORL database recognition are shown in Tables 7 and 8. Table 9 shows the calculation time for different methods to identify 160 images in the ORL dataset. The proposed BPLRC method also significantly outperformed the LRC, SRC, CRC, ESRC, and Module LRC (five-block processing) algorithms when using this database. The computation time of the BPLRC method is moderate, and its computational complexity is lower than that of the SRC and ESRC methods. Synthesizing the results of BPLRC on the AR, Extended Yale B, and ORL datasets demonstrated the effectiveness of the proposed BPLRC for solving face occlusion problem. Figure 14: Test samples with vertical occlusion of 10% and diagonal occlusion of 20%: (**a**,**b**) face images with 10% vertical occlusion; (**c**,**d**) face images with 20% diagonal occlusion [Reprinted with permission from Elsevier [20]. Copyright 2013, Neurocomputing]. Figure 15: Some images from the ORL database: (**a**) the ORL database without occluded images; (**b**) the ORL database with 10%-occluded images [Reprinted with permission from Elsevier [20]. Copyright 2013, Neurocomputing]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Occlusion Rate**} & \multirow{2}{*}{**Image Size**} & \multicolumn{6}{c}{**Method**} \\ \cline{3-8}  & & **LRC** & **SRC** & **CRC** & **ESRC** & **Module LRC** & **BPLRC** \\ \hline \multirow{3}{*}{10\%} & \(15\times 10\) & 83.13 & 63.13 & 73.13 & 78.75 & 39.38 & **93.75** \\  & \(20\times 15\) & 90.63 & 30.00 & 75.63 & 35.00 & 76.25 & **96.88** \\  & \(25\times 20\) & 91.25 & 69.38 & 83.13 & 75.63 & 82.50 & **95.63** \\ \hline \multirow{3}{*}{20\%} & \(15\times 10\) & 78.13 & 46.25 & 61.25 & 59.38 & 41.25 & **88.75** \\  & \(20\times 15\) & 82.50 & 15.63 & 68.75 & 23.75 & 78.13 & **93.13** \\  & \(25\times 20\) & 88.75 & 53.75 & 71.88 & 54.38 & 84.38 & **93.75** \\ \hline \multirow{3}{*}{30\%} & \(15\times 10\) & 56.25 & 36.25 & 50.00 & 51.51 & 37.50 & **85.63** \\  & \(20\times 15\) & 65.00 & 13.75 & 52.50 & 13.75 & 76.25 & **93.13** \\  & \(25\times 20\) & 69.38 & 43.13 & 58.13 & 43.75 & 80.00 & **91.88** \\ \hline \multirow{3}{*}{40\%} & \(15\times 10\) & 39.38 & 23.75 & 35.00 & 34.38 & 34.38 & **83.75** \\  & \(20\times 15\) & 45.00 & 10.00 & 40.00 & 11.88 & 75.63 & **88.13** \\ \cline{1-1}  & \(25\times 20\) & 46.25 & 29.38 & 48.13 & 29.38 & 83.75 & **88.75** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy (%) of different methods when identifying 10%-, 20%-, 30%-, and 40%-occluded face images from the ORL database. Figure 16: Test samples with vertical occlusion of 10% and diagonal occlusion of 20% from the ORL datasets: (**a**,**b**) face images with 10% vertical occlusion; (**c**,**d**) face images with 20% diagonal occlusion [Reprinted with permission from Elsevier [20], Copyright 2013, Neurocomputing].