## 5 Conclusions  
The method proposed in this paper combines local image information into a whole, reflecting both the local information and the overall information of the image. In the AR datasets, face images with scarves were downsampled to 25 \(\times\) 20 pixels as an example, and the recognition accuracy of the BPLRC algorithm in identifying the face images with scarf occlusion was 93.67%. The number of categories with precision, recall, and F1 score greater than 0.7 was 86, 94, and 93, respectively, and the number of categories with precision, recall, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **LRC [18]** & **SRC [10,11]** & **CRC [14]** & **ESRC [17]** & \begin{tabular}{c} **Module LRC** \\ [18] \\ \end{tabular} & **BPLRC** \\ \hline \multirow{3}{*}{\begin{tabular}{c} The basic \\ model \\ \end{tabular} } & Linear & Lasso & Ridge & Lasso & Linear & Linear \\  & regression & regression & regression & regression & regression & regression \\  & model & model & model & model & model & model \\ \hline \multirow{3}{*}{\begin{tabular}{c} Measuring \\ method \\ \end{tabular} } & Euclidean & Euclidean & Euclidean & \multirow{2}{*}{Euler distance} & Euclidean & Euclidean \\  & distance & distance & distance & distance & distance & distance \\ \hline \multirow{3}{*}{\begin{tabular}{c} Image \\ recognition \\ speed \\ \end{tabular} } & \multirow{3}{*}{Extremely fast} & \multirow{3}{*}{Extremely slow} & \multirow{3}{*}{Fast} & \multirow{3}{*}{Extremely slow} & \multirow{3}{*}{Relatively fast} & \multirow{3}{*}{Relatively slow} \\  & & & & & \\ \hline \multirow{3}{*}{Robustness} & \multirow{3}{*}{Extremely weak} & \multirow{3}{*}{Relatively weak} & \multirow{3}{*}{Relatively weak} & \multirow{3}{*}{Weak} & \multirow{3}{*}{\
\begin{tabular}{c} Relatively \\ strong \\ \end{tabular} } & \multirow{3}{*}{Strong} \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Scope \\ \end{tabular} } & 1. Face image & 1. Face image & 1. Face image & 1. Face image & 1. Face image & 1. Face image & 1. Face image & with light \\  & with light & with light & with light & changes; 2. face & changes; 2. face \\  & changes; 2. face & changes; 2. face & changes; 2. face & image with & image with \\  & image with & image with & image with & expression & expression \\  & expression & expression & changes; & changes; & changes; \\  & changes & changes & changes & 3. face & occlusion image \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Attribute comparison with prior methods. and F1 score equal to 0 was 1. These indicators can indicate the degree of excellence various algorithms have in identification. Furthermore, experiments on the Extended Yale B, ORL, and AR datasets showed that the BPLRC algorithm was significantly better than other related classification methods in identifying images with continuous occlusion. The LRC, SRC, ESRC, and CRC algorithms did not remove occlusions, which caused them to learn a lot of noise. As a result, model performance was worse than the Module LRC and BPLRC models. Although Module LRC removes the continuous occlusion part, it only considers retaining one block, and there may be less reserved effective face information than the BPLRC method. Therefore, the BPLRC algorithm's ability to identify face images is better than other related algorithms. BPLRC reorganizes different training samples and test samples through block arrangement and combination, but the number of combinations increases exponentially with the number of blocks. Compared to the LRC algorithm, the method proposed optimizes face characteristics in the image while at the same time reducing the negative impact of occlusion on the model. For example, the LRC method easily attributes face images with scarf occlusion to a large number of beards, or other categories that have characteristics similar to scarves. Compared to the Module LRC algorithm, this algorithm's novelty lies in retaining as many image block schemes as possible in order to retain useful face characteristics.