ADVANCEMENT IN CRIMINAL IDENTIFICATION FOR ENHANCED PUBLIC SAFETY: SVM-BASED FACE RECOGNITION WITH VGG ARCHITECTURE
DOI:
https://doi.org/10.35631/JISTM.1142024Keywords:
Criminal Identification, Facial Recognition, Royal Military Police (RMP), Support Vector Machine (SVM), Visual Geometry Group (VGG)Abstract
Face recognition technology helps Malaysia’s Royal Military Police (RMP) identify criminals faster. Manual identification at roadblocks makes errors and wastes time. Criminal activities are getting worse, but current identification systems do not work well enough. Better criminal identification systems have become necessary for police work. This research presents a Criminal Face Recognition System that identifies criminal faces using accurate image matching. The study improves public safety and supports RMP operations. Deep learning methods power the system, combining Support Vector Machine (SVM) with Visual Geometry Group (VGG) architecture. Test results show 93.50% accuracy, proving the system works well for recognizing known criminals and its strength when processing new faces. These technological advancement puts the RMP ahead in using new technology, showing their commitment to public safety and security. Installing such systems fixes current identification problems while giving police reliable tools for catching criminals. Police need modern solutions that work against changing criminal methods and this system provides those tools.
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