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.