Masked Face Image Classification with Sparse Representation based on Majority Voting Mechanism
This work addresses masked face recognition, a domain-specific problem, but is incremental as it combines existing methods.
The paper tackled masked face image classification by implementing Orthogonal Matching Pursuit and Sparse Representation-based Classification with a majority voting mechanism, achieving an accuracy of 98.4% on the AR dataset.
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%.