欠完备采样环境下面向数据的稀疏表示人脸识别研究
[Abstract]:Face recognition is widely studied because of its non-contact and easy acquisition. It is mainly applied to the system of attendance, entrance guard, monitoring and public security. Although a large number of face recognition algorithms have acquired better recognition performance, the face recognition system still faces many challenges in the practical application, which mainly comprises the problems of shielding face recognition caused by illumination changes, ornaments, and the like; and the number of samples that can be acquired under the non-controllable condition is small, In this paper, the face recognition in this case is called the under-complete sampling face recognition. Under-complete sampling can cause the loss of face information and reduce the recognition performance of the existing algorithm. To this end, a sparse representation algorithm for data is proposed to study the problem to improve the robustness and practicability of the face recognition algorithm. (1) based on the nearest neighbor representation and the resolution decomposition algorithm, a sparse representation image recognition algorithm based on a neighbor class weighting structure and a sparse representation shielding face recognition algorithm based on the resolution decomposition structure are respectively proposed. The contribution of various training samples in the dictionary to the classification of the test samples is different, and the common neighbor samples have a great effect on the correct classification of the test samples, therefore, considering the selection of the nearest neighbor class and weighting the test sample classification, not only can the computational complexity of the algorithm be reduced, Improve that recognition rate of the algorithm at the same time. In addition, in order to improve the face recognition performance in the case of occlusion, the shielding part is separated by a resolution decomposition algorithm, and the main component analysis is carried out on the common part and the low-rank condition part obtained by the decomposition, and the projection matrix is calculated, And finally, structural sparse representation and classification are carried out on the projection space. (2) in order to solve the sensitivity of the global algorithm to the occlusion, and further reduce the influence of the occlusion on the recognition performance, the image is segmented and partially processed, and the high-weight value is given by the clean module to reduce the influence of the blocking module on the performance of the algorithm by giving a low weight to the shielding module. To this end, several different module weighting schemes are proposed: first, the image is divided into a plurality of modules with overlapping, and the resolution of each module is calculated by using the Fisher rate, the method comprises the following steps of: dividing an image into four parts, weighting the module by using a sparse residual error to estimate the shielding part, and finally carrying out classification judgment on the non-shielding part; and finally, combining the two weighting schemes, and putting forward a module weighting algorithm based on the Fisher discrimination and the sparse residual, The algorithm combines the advantages of Fisher's weight and residual weight to further improve the shielding performance. And (3) in order to accurately detect the occlusion region and realize the occlusion face recognition on the non-occlusion training set, the occlusion detection algorithm on the two pixel levels is proposed, namely, the pixel-level occlusion detection face recognition based on the sparse representation and the double-layer sparse representation classification algorithm based on the block recursive residual analysis. The pixel-level occlusion detection algorithm based on the sparse representation analyzes the various occlusion estimation results according to the class residual, and then counts the results to obtain the final pixel occlusion estimation, and finally, the pixel-level occlusion detection algorithm is only identified on the non-occlusion pixel set. Based on the algorithm of the block recursive residual analysis, the occlusion sample is divided into upper and lower modules, the whole image is reconstructed by using a module with higher sparsity, and the occlusion pixel is estimated to be weighted and classified according to the residual estimated occlusion pixel so as to improve the identification performance of the occlusion face. The pixel-level occlusion detection can avoid the problem of low recognition rate caused by the blocking and non-blocking part in the module in the block-blocking detection algorithm. (4) The kernel space is used for non-linear expansion of the block sparse representation algorithm, and a kernel block sparse representation algorithm (KBSRC: Kernel Block Sparse Representation based Classification) is proposed, and the sample is projected into the reduced-dimension nuclear space, so that the original non-linear space of the sample can be linearized, And the classification performance can be improved by using the structure information classification of the samples in the space.
【学位授予单位】:燕山大学
【学位级别】:博士
【学位授予年份】:2016
【分类号】:TP391.41
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