Face Recognition Using Support Vector Machine (SVM) and Backpropagation Neural Network (BNN) Methods to Identify Gender on Student Identity Cards

Abstract

Until now, many people continue to explore studies on
facial recognition, as reflected in the advancements of Computer
Vision technology implemented in various real-life applications.
This research aims to identify a person's face based on
characteristics or gender features found on student identity cards
at a university. The method employed involves a data science or
machine learning approach, using the SEMMA model (Sample,
Explore, Modify, Model, and Assess) with the application of two
algorithms, namely Support Vector Machine (SVM) and
Backpropagation Neural Network (ANN). This modeling is
further reinforced by pre-processing using Principal Component
Analysis (PCA) to reduce the dimensions of various image
features to selected features. The research results indicate
improved performance, with accuracy reaching 77.50% for the
SVM algorithm and 78.10% for ANN. This performance is
superior to previous studies that did not involve dimension
reduction techniques using PCA.