论文标题
基于多数投票机制的蒙版面部图像分类，稀疏表示
Masked Face Image Classification with Sparse Representation based on Majority Voting Mechanism
论文作者
论文摘要
稀疏近似是找到来自冗余词典的信号的最稀少线性组合的问题，该字典被广泛应用于信号处理和压缩感测。在此项目中，我设法实施了基于稀疏表示的算法（OMP）算法（OMP）算法（SRC）算法，然后使用它们来完成以多数投票的掩盖图像分类任务。在这里，实验是在AR数据集上的令牌，结果显示了OMP算法的优越性与SRC算法相对于蒙面的面部图像分类，精度为98.4％。
Sparse approximation is the problem to find the sparsest linear combination for a signal from a redundant dictionary, which is widely applied in signal processing and compressed sensing. In this project, I manage to implement the Orthogonal Matching Pursuit (OMP) algorithm and Sparse Representation-based Classification (SRC) algorithm, then use them to finish the task of masked image classification with majority voting. Here the experiment was token on the AR data-set, and the result shows the superiority of OMP algorithm combined with SRC algorithm over masked face image classification with an accuracy of 98.4%.
