Masked Face Image Classification with Sparse Representation based on Majority Voting Mechanism
Computer Vision and Pattern Recognition
2020-11-10 v1
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%.
Cite
@article{arxiv.2011.04556,
title = {Masked Face Image Classification with Sparse Representation based on Majority Voting Mechanism},
author = {Han Wang},
journal= {arXiv preprint arXiv:2011.04556},
year = {2020}
}