###### Abstract
To solve the problem of abnormal features in the RBCM, many research groups have proposed related robust algorithms, among which the Sparse Representation-based Classification (SRC) algorithm was among the first proposed [10; 11]. The biggest feature of this type of algorithm is its ability to linearly represent the test samples by building a dictionary containing all the training samples. The relatively large time complexity of the SRC algorithm in solving the L1 norm optimization problem has largely limited its applications. However, [PERSON] et al. [14] proposed the Collaborative Representation-based Classification (CRC) algorithm. The SRC and CRC algorithms both use training samples from all categories to linearly represent test samples. However, the biggest difference between them is that the CRC algorithm uses the less computationally intensive L2 norm instead of the L1 norm, as used in the SRC algorithm, to solve the optimization problem. Some scholars have proposed a Two-Phase Test Sample Sparse Representation (TPTSSR) algorithm based on the CRC algorithm [15]. In the first stage of the algorithm, CRC was used to select M training samples for the best representation of test samples. In the second stage, the CRC algorithm was used again to identify the test samples, and the training set was the M training samples determined in the previous stage. The training samples selected in the first stage of TPTSSR cannot improve the accuracy of CRC for face image recognition. Thus, [PERSON] et al. [16] further improved the algorithm and proposed the Random-filtering-based Sparse Representation (RFSR) algorithm. [PERSON] et al. [17] improved the distance metric of the SRC algorithm. According to the author's experiments, using cosine or Euler distance as the measure can expand the inter-sample and intra-class distance simultaneously, and the multiple inter-class distance expansion was much higher than the multiple intra-class distance expansion, which was conducive to improving the robustness of the SRC algorithm. The various RBCM methods proposed in the literature [10; 11; 12; 13; 14; 15; 16; 17] constrained the model coefficients to float in a small range, reducing the negative impact of face occlusion features on the model coefficients. Constraining the model coefficient through the L1 and L2 norm cannot improve model identification performance. Only when the model is constrained to learn as small a number of occlusion features as possible is model performance made stable. In addition to using all classes of training samples to represent test samples linearly, representation-based classification methods can also use a single class of training samples to represent the test samples. Linear Regression Classification (LRC) [18] uses a single category of training samples to reconstruct the samples to be tested. LRC can be viewed as a representation based on the L2 norm, which uses the classification rules of the nearest subspace to classify face images. LRC finally selects the subspace with the smallest distance by projecting the test image onto the subspace. For this, the decision method is based on the distance metric, which is unsuitable for dealing with continuous severe occlusion problems. The LRC method is similar to the SRC and CRC methods, but the LRC method does not restrict the representation coefficient. Therefore, if there are many abnormal variables in the training or test sample, the LRC method can learn a lot of abnormal information. As a result, the model classification fails. Therefore, some researchers, such as the authors of [18], have adopted the method of modular [19] image representation, which has made LRC more promising when trying to accurately identify face images with facial occlusions. The modular LRC algorithm [18] first worked by segmenting a given occlusion image. The \"good or bad\" image patch was then judged by the distance metric of the intermediate decision, and the \"best\" image patch was selected.