## 3 Results
### Robust Regression Model Based on Occlusion Training  
In practice, there may be too few images in each object, and these images contain a lot of non-face information. A face image with a scarf-occluded face was used as the training sample, and a face image without occlusion but with different lighting conditions was used as the recognition object (see Figure 10). Table 3 shows the results of identifying downsampled images with a resolution of 25 \(\times\) 20 pixels. The results in Table 3 and Figure 11 prove that the algorithm has strong robustness and outperforms other algorithms, even with a small number of training samples.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Occlusion Test**} & \multirow{2}{*}{**Image Size**} & \multicolumn{6}{c}{**Method**} \\ \cline{3-8}  & & **LRC** & **SRC** & **CRC** & ** ESRC** & **Module LRC** & **BPLRC** \\ \hline \multirow{3}{*}{Scarf occlusion} & \(15\times 10\) & 8.33 & 31.33 & 25.67 & 7.67 & 37.00 & **85.33** \\  & \(20\times 15\) & 10.33 & 42.67 & 46.67 & 18.33 & 60.67 & **91.33** \\  & \(25\times 20\) & 11.33 & 48.00 & 61.67 & 22.33 & 67.33 & **93.67** \\ \hline \multirow{3}{*}{Sunglasses occlusion} & \(15\times 10\) & 37.00 & 25.00 & 20.33 & 24.00 & 71.67 & **85.67** \\  & \(20\times 15\) & 51.33 & 48.00 & 46.33 & 38.67 & 86.00 & **91.00** \\  & \(25\times 20\) & 54.33 & 47.33 & 47.00 & 39.67 & 88.00 & **90.67** \\ \hline \multirow{3}{*}{Mixed occlusion} & \(15\times 10\) & 22.67 & 28.17 & 23.00 & 15.84 & 54.34 & **85.50** \\  & \(20\times 15\) & 30.83 & 45.34 & 46.50 & 28.50 & 73.34 & **91.17** \\ \cline{1-1}  & \(25\times 20\) & 32.83 & 47.67 & 54.34 & 31.00 & 77.67 & **92.17** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy (%) of different methods when identifying scarf occlusion, sunglasses occlusion, and mixed occlusion images.  
\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 \(15\times 10\) & **0.46** & 55.46 & 0.78 & 61.03 & 2.36 & 15.83 \\ \(20\times 15\) & **0.46** & 143.15 & 0.78 & 140.65 & 3.49 & 21.74 \\ \(25\times 20\) & **0.61** & 323.80 & 0.98 & 329.51 & 4.92 & 28.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational time (seconds) for different methods to identify 300 images.  
Figure 11: The relationship between the recognition rate of different methods for face images with scarves (the images were downsampled to 500 dimensions) and the number of training samples per class [authors’ own processing].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Number of Training Samples Per Class**} \\ \cline{2-5}  & **2** & **3** & **4** & **5** & **6** \\ \hline LRC & 6.75 & 7.00 & 10.75 & 9.88 & **10.88** \\ SRC & 3.88 & 2.38 & 3.88 & 10.88 & **26.00** \\ CRC & 24.00 & 28.63 & 51.13 & 59.25 & **61.00** \\ Module LRC & 20.25 & 49.13 & 63.00 & 73.00 & **80.75** \\ BPLRC & 32.75 & 59.88 & 74.75 & 83.88 & **89.25** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of different methods when recognizing face images with scarves (images are downsampled to 500 dimensions).  
Figure 10: Some face images from the AR subset: (**a**) the left picture shows some of the training images used in the experiment; (**b**) the right picture shows some of the test samples used in the experiment [Reprinted with permission from Elsevier (2017)]. Copyright 2013, Neurocomputing].