Authors :
Ismaila Folasade. M.; Afolabi O. Adeolu; Ismaila W. Oladimeji; Alo Oluwaseun O.
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/umm9z7xk
Scribd :
http://tinyurl.com/y8e7w7t4
DOI :
https://doi.org/10.5281/zenodo.10670429
Abstract :
Digital image processing is the use of
computer algorithms to analyze digital images. Digital
image processing, involves many processing stages of
which feature extraction stage is important. Feature
extraction involves reducing the number of resources
required to describe a large set of data. However,
choosing a feature extraction techniques is a problem
because of their deficiencies. Thus, this paper presents a
comparative performance analysis of selected feature
extraction techniques in human face images. 90 face
images were acquired with three different poses viz:
normal, angry and laughing. The face images were first
pre-processed and then subjected to selected feature
extraction techniques (Local binary pattern, Principal
component analysis, Gabor filter and Linear
discriminant analysis). The extracted features were then
classified using Backpropagation neural network. The
results of recognition accuracy produced by Gabor filter,
PCA, LDA and LBP at 0.76 threshold are 76.7%, 72.2%.
78.9% and 85.6%. Hence, it can be deduced that LBP
performed the best among the four selected feature
extraction techniques.
Keywords :
Digital Image Processing, Feature Extraction, Local Binary Pattern, Principal Component Analysis, Gabor Filter, Linear Discriminant Analysis.
Digital image processing is the use of
computer algorithms to analyze digital images. Digital
image processing, involves many processing stages of
which feature extraction stage is important. Feature
extraction involves reducing the number of resources
required to describe a large set of data. However,
choosing a feature extraction techniques is a problem
because of their deficiencies. Thus, this paper presents a
comparative performance analysis of selected feature
extraction techniques in human face images. 90 face
images were acquired with three different poses viz:
normal, angry and laughing. The face images were first
pre-processed and then subjected to selected feature
extraction techniques (Local binary pattern, Principal
component analysis, Gabor filter and Linear
discriminant analysis). The extracted features were then
classified using Backpropagation neural network. The
results of recognition accuracy produced by Gabor filter,
PCA, LDA and LBP at 0.76 threshold are 76.7%, 72.2%.
78.9% and 85.6%. Hence, it can be deduced that LBP
performed the best among the four selected feature
extraction techniques.
Keywords :
Digital Image Processing, Feature Extraction, Local Binary Pattern, Principal Component Analysis, Gabor Filter, Linear Discriminant Analysis.
