###### Abstract
Finally, converting the block into individual decisions was considered the final classification result. The main advantage of this method is its equivalency when dynamically removing occluded partitions. However, the biggest drawback of modular LRC is that it uses only specific blocks (with minimal residuals), discarding blocks that contain other useful face information. At this point, how to extract effective face characteristics and remove occlusion has become a question that researchers [20; 21] care about. This paper proposes the Block Permutation Linear Repression Classification (BPLRC) algorithm. The proposed method first modulates the image and then groups the schemes that retain the same number of blocks. Next, their residuals are compared to determine the permutation of the group and the group with the best recognition effect is finally selected. Compared to the modular LRC algorithm, the proposed method retains more useful face information and also achieves the purpose of removing invalid occlusion blocks. Moreover, there is no need to judge the occlusion ratio to achieve the best recognition effect. The algorithm improves the robustness of LRC to recognize the occluded image to a certain extent. The RBCM methods used in this article mainly include LRC, SRC, CRC, Euler Sparse Representation-based Classification (ESRC [17]), Module LRC, and the BPLRC method proposed in this article. These methods are the same as linear models, and the type of linear representation is the difference. LRC uses a single class of travelers to reply to the test samples. SRC, ESRC, and CRC methods use all-class training samples to indicate test samples linearly. Among them, SRC and ESRC have differences in distance measurement. The difference between the SRC and CRC algorithm is that SRC uses the L1 model to restrain the sparse coefficient, and CRC uses the L2 model to restrain the sparse coefficient. Module LRC selects the \"most useful\" block as a feature input of the LRC algorithm through blocking. The difference from the LRC algorithm is that the Module LRC is used to screen the effective face characteristics as much as possible during identification, and the LRC algorithm simply uses all image features in identification. The BPLRC algorithm proposed in this article is similar to Module LRC. All images are modularized, and the identification of LRC algorithms characterizes effective face characteristics. The difference between the BPLRC algorithm and the Module LRC algorithm is that the Module LRC only retains one block, while the BPLRC method considers all the segmentation arrangement schemes to retain as much information as possible. From a principle point of view, all block arrangement schemes in BPLRC include the full-retained LRC and the Module LRC method that only retains one block. The rest of this article is organized as follows: Section 2 details the principles of the LRC, SRC, CRC, and BPLRC algorithms, with a slight reference to ESRC and Module LRC. Section 3 describes the decision principles and experimental results of the proposed method. Section 4 discusses the contrast between BPLRC and other related algorithms in relation to the other performance metrics of the model. Section 5 is the conclusion of this paper.