Masked Face Image Classification with Sparse Representation based on
Majority Voting Mechanism
- URL: http://arxiv.org/abs/2011.04556v1
- Date: Mon, 9 Nov 2020 16:55:14 GMT
- Title: Masked Face Image Classification with Sparse Representation based on
Majority Voting Mechanism
- Authors: Han Wang
- Abstract summary: I implement the Orthogonal Matching Pursuit (OMP) algorithm and Sparse Representation-based Classification (SRC) algorithm.
The result shows the superiority of OMP algorithm combined with SRC algorithm over masked face image classification with an accuracy of 98.4%.
- Score: 4.451150873349085
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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%.
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