## 3 Results
### Continuous Occlusion Result for the AR Database  
According to the above selection, the optimal block arrangement and combination was applied to the AR datasets (this method was also applied to the Extended Yale B and ORL datasets later). First of all, for data processing, the experiment divided the test analysis into three parts. The first part was the recognition of face images occluded by scarves. The second part was the recognition of face images occluded by sunglasses. The third part was the analysis of face images occluded by scarves and by sunglasses. When the BPLRC algorithm was applied, the face images were all horizontally divided into five blocks. The results regarding AR dataset recognition are shown in Table 1. In the table, ESRC stands for the Euler Sparse Representation Classification (ESRC) [17] algorithm. The experimental parameters were set according to [17], where \(\lambda=1.9\) and \(\alpha=0.5\). The results showed that the proposed BPLRC method was significantly better than the LRC, SRC, CRC, ESRC, and Module LRC algorithms in the three parts of the AR database. Among them, the LRC method was easily affected by the blocking features. In the AR datasets, the scar part of the test image was linked to the characteristics of the male beard in the training  
Figure 8: Residual calculation of arrangement scheme a (scarves occluding the face). [Reprinted with permission from Elsevier [20]. Copyright 2013, Neurocomputing].  
Figure 9: Residuals of face images involving scarves with different permutations [authors’ own processing].  
image. Learning the wrong characteristics caused model recognition performance to be poor. Although the SRC, ESRC, and CRC algorithms can also connect the scarf parts with the characteristics of a male beard, the three coefficients will be constrained through the L1 and L2 models, thus limiting their ability to learn in the wrong direction to a certain extent. Therefore, the accuracy rate of the SRC, ESRC, and CRC algorithms in terms of recognizing scarves covering human faces in images was higher than the LRC algorithm. The Module LRC and BPLRC methods extract the effective face characteristics as much as possible, and they will thus only learn a little noise information, as with the LRC, SRC, ESRC, and CRC algorithms. The BPLRC method is similar to Module LRC. The former considers retaining more blocks. However, face information was more effective than the Module LRC method, so the linear models learned more face characteristics. Therefore, the BPLRC algorithm identifies the effect of occlusion in face images better than other related algorithms.  
The LRC and CRC methods are relatively simple, and the calculation time is short. For 25 \(\times\) 20 pixels images, the average time for each image with the LRC and CRC methods was 0.61 s and 0.98 s. The advantage of the LRC method is that it is very simple to calculate; however, it is easily affected by abnormal values. When the test graph contains continuous occlusion, recognition performance decreases sharply. The BPLRC method proposed in this article improves its robustness based on LRC, but the calculation cost increases. From Table 2, it was found that the calculation time of SRC and ESRC was much higher than the BPLRC method, and Table 1 did not show robustness higher than BPLRC.