Abstract:
Face recognition is a well-known image analysis
application in the fields of pattern recognition and computer
vision. It utilizes the uniqueness of human facial characteristics
for personnel identification. The paper in hand presents a facial
recognition system that uses facial features and Support Vector
Machine (SVM) to achieve accurate recognition. The image
processing happens with histogram equalization, also utilizing a
median filter for pre-processing. A combination of wavelet
transforms and Histograms of Oriented Gradients (HOG),
extracts the feature vector, which produces a reliable
performance. Dimensionality reduction happens by applying
Principal Component Analysis (PCA). Finally, by applying the
Support Vector Machine (SVM), face classification happens.
Experimental development happened on the Yale database of
165 images from 15 individuals in a MATLAB environment.
The testing proved the accuracy of 98.64% percentage of success
with acceptable speed, and it confirmed the accuracy and
robustness of the proposed system.