The Using of PCA, Wavelet and GLCM In Face Recognition System, A Comparative Study

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Manar Abdulkaream Al-Abaji
Meaad Mohammed Salih

Abstract

The process of data dimension reduction plays an important role in any  face recognition system because many of these data are repetitive and irrelevant and this cause a problem in applications of data mining and learning the machine. The main purpose is to improve the performance of recognition by eliminating repetitive features.


          In this research, a number of data reduction techniques were used like: Principal Component Analysis, Gray-Level Co-occurrence Matrix and Discrete Wavelet Transform for extracting the most important features from the images of persons. A different number of training and testing images were used to compare the performance of each of the techniques above in the recognition process. Euclidean distance scale was used to get results.


 

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How to Cite
[1]
M. A. Al-Abaji and M. M. Salih, “The Using of PCA, Wavelet and GLCM In Face Recognition System, A Comparative Study”, JUBPAS, vol. 26, no. 10, pp. 131 - 139, Dec. 2018.
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