基于稀疏表示的代价敏感性人脸识别算法研究模式识别与智能系统专业论文.docx

南京邮电大学硕士研究生学位论文ABS 南京邮电大学硕士研究生学位论文 ABSTRACT ABSTRACT As one of the most popular research topics, sparse representation technique has been successfully employed to solve face recognition task. Though current sparse representation based methods prove to achieve high classification accuracy, they implicitly assume that the losses of all misclassifications are the same. However, in many real-world applications, different misclassifications could lead to different losses. Driven by this concern, we propose in this paper two sparse representation based cost-sensitive algorithms for face recognition. We first propose a sparse representation-based cost-sensitive classifier (SRCSC). SRCSC uses probabilistic model of sparse representation to estimate the posterior probabilities, separately calculates all the misclassification losses via the posterior probabilities and then predicts the class label by minimizing the losses. Our main contribution is to extend the sparse representation technique to cost-sensitive classifier. We then propose a cost-sensitive sparsity preserving projections (CSSPP). CSSPP considers the cost information of sparse representation while calculating the sparse structure of the training set. Then, CSSPP employs the sparsity preserving projections method to achieve the projection transform and keeps the sparse structure in the low-dimensional space. In addition, we analyze the relationship between cost and the sparse coefficients. Experimental results on the public AR and FRGC face databases show that both of the proposed approaches can achieve high recognition rate and low misclassification loss, which validate the efficacy of the proposed approaches. Key words：cost-sensitive learning, sparse representation, cost-sensitive classifier, feature extraction, face recognition - II - 南京邮电大学硕士研究生学位论文目录 南京邮电大学硕士研究生学位论文 目录 - - PAGE IV - 目 录 摘 要 I ABSTRACT II 目 录 III 第一章 绪论 1 1.1 课题研究背景及意义 1 1.2 国内外研究概述及发展历史 3 1.3 人脸识别方法概述 4 1.3.1 基于几何特征的方法 5 1.3.2 基于代数特征的方