PCA-based dimensionality reduction for face recognition
In this paper, we conduct a comprehensive study on dimensionality reduction (DR) techniques and discuss the mostly used statistical DR technique called principal component analysis (PCA) in detail with a view to addressing the classical face recognition problem. Therefore, we, more devotedly, propos...
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| Veröffentlicht in: | Telkomnika 2021-10, Vol.19 (5), p.1622-1629 |
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| container_title | Telkomnika |
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| creator | Marjan, Md. Abu Islam, Md. Rashedul Uddin, Md. Palash Afjal, Masud Ibn Mamun, Md. Al |
| description | In this paper, we conduct a comprehensive study on dimensionality reduction (DR) techniques and discuss the mostly used statistical DR technique called principal component analysis (PCA) in detail with a view to addressing the classical face recognition problem. Therefore, we, more devotedly, propose a solution to either a typical face or individual face recognition based on the principal components, which are constructed using PCA on the face images. We simulate the proposed solution with several training and test sets of manually captured face images and also with the popular Olivetti Research Laboratory (ORL) and Yale face databases. The performance measure of the proposed face recognizer signifies its superiority. |
| doi_str_mv | 10.12928/telkomnika.v19i5.19566 |
| format | Article |
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| ispartof | Telkomnika, 2021-10, Vol.19 (5), p.1622-1629 |
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| language | eng |
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| source | Free E-Journal (出版社公開部分のみ） |
| subjects | Data mining Datasets Direct reduction Discriminant analysis Face recognition Feature selection Laboratories Machine learning Methods Principal components analysis Variables Visualization |
| title | PCA-based dimensionality reduction for face recognition |
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