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Masked Face Image Classification with Sparse Representation based on Majority Voting Mechanism

2020-11-09 16:55:14
Han Wang
arXiv_CV
           
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%.

Abstract (translated)

URL

https://arxiv.org/abs/2011.04556

PDF

https://arxiv.org/pdf/2011.04556.pdf

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