It is of paramount importance to establish an identity of citizenry to curb criminalities. Principal Component Analysis (PCA) which is one of the foremost methods for feature extraction and feature selection is adopted for identification and authentication of people. The computational time used by PCA is too much and Chinese Remainder Theorem was employed to reduce its computational time. TOAM database was setup which contained 120 facial images of 40 persons frontal faces with 3 images of each individual. 80 images were used for training while 40 were used for testing. Training time and testing time were used as performance metrics to determine the effect of CRT on PCA in terms of computational time. The experimenal results indicated an average training time of 13.5128 seconds and average testing time of 1.5475 second for PCA while PCA-CRT average training time is 13.2387 seconds and average testing time of 1.5185 seconds. Column chart was used to show the graphical relationship between PCA and PCA-CRT Training time and testing time. The research revealed that CRT reduce PCA computational time.
| Published in | International Journal of Discrete Mathematics (Volume 4, Issue 1) |
| DOI | 10.11648/j.dmath.20190401.11 |
| Page(s) | 1-7 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2019. Published by Science Publishing Group |
Dimensionality Reduction, Chinese Remainder Theorem, Eigenface, Training Time, Database
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APA Style
Madandola Tajudeen Niyi, Gbolagade Kazeem Alagbe. (2019). Reducing Computational Time of Principal Component Analysis with Chinese Remainder Theorem. International Journal of Discrete Mathematics, 4(1), 1-7. https://doi.org/10.11648/j.dmath.20190401.11
ACS Style
Madandola Tajudeen Niyi; Gbolagade Kazeem Alagbe. Reducing Computational Time of Principal Component Analysis with Chinese Remainder Theorem. Int. J. Discrete Math. 2019, 4(1), 1-7. doi: 10.11648/j.dmath.20190401.11
AMA Style
Madandola Tajudeen Niyi, Gbolagade Kazeem Alagbe. Reducing Computational Time of Principal Component Analysis with Chinese Remainder Theorem. Int J Discrete Math. 2019;4(1):1-7. doi: 10.11648/j.dmath.20190401.11
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Download@article{10.11648/j.dmath.20190401.11,
author = {Madandola Tajudeen Niyi and Gbolagade Kazeem Alagbe},
title = {Reducing Computational Time of Principal Component Analysis with Chinese Remainder Theorem},
journal = {International Journal of Discrete Mathematics},
volume = {4},
number = {1},
pages = {1-7},
doi = {10.11648/j.dmath.20190401.11},
url = {https://doi.org/10.11648/j.dmath.20190401.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.dmath.20190401.11},
abstract = {It is of paramount importance to establish an identity of citizenry to curb criminalities. Principal Component Analysis (PCA) which is one of the foremost methods for feature extraction and feature selection is adopted for identification and authentication of people. The computational time used by PCA is too much and Chinese Remainder Theorem was employed to reduce its computational time. TOAM database was setup which contained 120 facial images of 40 persons frontal faces with 3 images of each individual. 80 images were used for training while 40 were used for testing. Training time and testing time were used as performance metrics to determine the effect of CRT on PCA in terms of computational time. The experimenal results indicated an average training time of 13.5128 seconds and average testing time of 1.5475 second for PCA while PCA-CRT average training time is 13.2387 seconds and average testing time of 1.5185 seconds. Column chart was used to show the graphical relationship between PCA and PCA-CRT Training time and testing time. The research revealed that CRT reduce PCA computational time.},
year = {2019}
}
TY - JOUR T1 - Reducing Computational Time of Principal Component Analysis with Chinese Remainder Theorem AU - Madandola Tajudeen Niyi AU - Gbolagade Kazeem Alagbe Y1 - 2019/03/30 PY - 2019 N1 - https://doi.org/10.11648/j.dmath.20190401.11 DO - 10.11648/j.dmath.20190401.11 T2 - International Journal of Discrete Mathematics JF - International Journal of Discrete Mathematics JO - International Journal of Discrete Mathematics SP - 1 EP - 7 PB - Science Publishing Group SN - 2578-9252 UR - https://doi.org/10.11648/j.dmath.20190401.11 AB - It is of paramount importance to establish an identity of citizenry to curb criminalities. Principal Component Analysis (PCA) which is one of the foremost methods for feature extraction and feature selection is adopted for identification and authentication of people. The computational time used by PCA is too much and Chinese Remainder Theorem was employed to reduce its computational time. TOAM database was setup which contained 120 facial images of 40 persons frontal faces with 3 images of each individual. 80 images were used for training while 40 were used for testing. Training time and testing time were used as performance metrics to determine the effect of CRT on PCA in terms of computational time. The experimenal results indicated an average training time of 13.5128 seconds and average testing time of 1.5475 second for PCA while PCA-CRT average training time is 13.2387 seconds and average testing time of 1.5185 seconds. Column chart was used to show the graphical relationship between PCA and PCA-CRT Training time and testing time. The research revealed that CRT reduce PCA computational time. VL - 4 IS - 1 ER -
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