Reducing Computational Time of Principal Component Analysis with Chinese Remainder Theorem

Madandola Tajudeen Niyi,Gbolagade Kazeem Alagbe

doi:10.11648/j.dmath.20190401.11

Madandola Tajudeen Niyi, Gbolagade Kazeem Alagbe

Open Access

https://doi.org/10.11648/j.dmath.20190401.11

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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.

Highlights

Reference [8] pointed out that establishing identity of an individual is of paramount importance in extremely interrelated organization
Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two foremost methods used for feature extraction and feature selection in the appearance-based methods
It has been discussed by Reference [1] that when the training dataset is small, PCA can outperform LDA, and that PCA is less sensitive to different training datasets

Summary

Introduction

Reference [8] pointed out that establishing identity of an individual is of paramount importance in extremely interrelated organization It is of greatest important because of increase in daily criminal acts in the world today especially in Nigeria. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two foremost methods used for feature extraction and feature selection in the appearance-based methods. It has been discussed by Reference [1] that when the training dataset is small, PCA can outperform LDA, and that PCA is less sensitive to different training datasets. Principal Component Analysis is a powerful dimensionality reduction algorithm and it is reliable in face recognition system but its computational time needs to be reduced. Reference [4] described PCA as a technique for reducing the dimensionality of datasets, increasing interpretability but at the same time minimizing information loss

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