Feature extraction using sparse component decomposition for face classification

Document Type : Original Article

Author

Assistance Prof. Electrical and Computer Engineering Department,, Hamedan University of tecnology. Hamedan, Iran. Email: h.doosti@hut.ac.ir

Abstract

In the recent years, the feature extraction as an intermediate step in the classification, has attracted the attention of researchers. In this paper, a new supervised feature extraction method is proposed using sparse component decomposition. The proposed algorithm has two steps.In the first step, the common information of the data matrix is extracted in a low rank matrix. In he second step, a linear feature extractor method such as local preservation projection one is used to extract the final features. Then, the extracted features are fed to the support vector machine classifier. To evaluate the accuracy rate of the proposed method, three datasets are used. The results show that the proposed method outperforms compared with some state of the art methods.

Keywords

References

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Engineering Management and Soft Computing
Volume 9, Issue 1 - Serial Number 16
November 2023
Pages 64-74

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