  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%.
