# Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition
## 2 Related Works  
[PERSON] et al. proposed a classification method based on sparse representation classifier (SRC) [21], which has been experimentally proven to have better performance than classifier-based algorithms for partially occluded samples. Due to their satisfactory performance, various extensions of the SRC have been proposed [22; 23; 24; 25]. [PERSON] et al. [26] also put forward the superposition of SRC (SSRC). However, ESRC and SSRC are slow in classification due to the large number of atoms. The work of robust kernel representation with statistical local features (SLF-RKR) [27] and regularized robust coding (RRC) [28] is based on extended SRC, which improves the performance of occluded samples, but they also reduce the performance of non-occluded samples due to overfitting problems. [PERSON] et al. [24] developed a face recognition method that uses a multiscale retinex(MSR) to obtain a similarity matrix and construct a weighted kernel sparse representation. [PERSON] et al. [23] developed a new robust, discriminative and comprehensive dictionary that can learn commonality, class specialty and interference, which effectively strengthens the classification effect of the dictionary. [PERSON] et al. [25] proposed a linear representation classifier based on superposition through the centroid and internal association of the class. Kernel discriminant analysis (KDA) [29] is a kernel-based face recognition method that projects data into nondiscriminant subspaces. KDA has seen many improvements in recent years. [PERSON] et al. [30] developed a multiple orientation and scale transforms (MOST) algorithm based on Gabor kernel transform. [PERSON] et al. [31] proposed a multicore discriminative dictionary learning method that combines multifeature kernel learning with dictionary learning techniques to improve the recognition rate. [PERSON] et al. [32] proposed a face recognition system that combines k-NearestNeighbor (kNN)), KDA and SVM. [PERSON] et al. [33] presented a virtual dictionary that can automatically generate new training samples in the training set to construct a model, which solves the problem of small training samples. It is noted that KDA performs better in handling nonoccluded samples, but for an occluded face image, the performance of KDA is worse than SRC. [PERSON] et al. [34] proposed a kernel extend dictionary(KED) to solve the problem that KDA cannot suppress occlusion variations well. KED has similarities with KRDD, but there are also essential differences. First of all, KED also uses KDA and SRC algorithms, which makes KED and KRDD models have similarities. However, the KRDD model has one more layer structure than KED, so it is more complicated. Secondly, the KED model has only the extended dictionary besides the basic dictionary, and the KRDD model has the real disturbance dictionary and the simulated disturbance dictionary in addition to the basic dictionary. Finally, and the most importantly, KRDD has a series of processed alternative training samples in addition to original training samples. This allows KRDD to process a larger amount of noisy data and make the layering of the data clearer. Since KDA and SRC have their advantages in different aspects, in this paper, a kernel-based robust disturbance dictionary (KRDD) is proposed for face recognition, which is an effective approach to combine KDA, SRC, and KPCA. It is noteworthy that the KRDD is not a simple addition of these algorithms, as it constructs two species disturbance dictionaries to effectively cope with variations in the occlusion samples, which makes the disturbance dictionary more robust. The image in the gallery sample is not the same as the image in the training sample. KRDD has better adaptability as it can handle various situations according to the actual conditions. The contributions of this paper are summarized below:  
1. A kernel-based robust disturbance dictionary (KRDD) is proposed. The KRDD consists of two types of disturbance dictionaries that can effectively represent more types of occlusion variations. 2.