AI Powered Enhanced Facial Recognition System for Law Enforcement Operations

Abstract

Facial recognition technology has emerged as a critical component in modern surveillance and law enforcement systems. With the rapid advancement of Artificial Intelligence (AI) and Deep Learning (DL), automated identification systems have significantly improved in terms of accuracy and efficiency. However, traditional deep learning models often struggle to maintain consistent performance under real-world conditions such as illumination variation, pose differences, occlusion, and background noise. These challenges limit their effectiveness in practical deployment scenarios. To address these limitations, this work proposes an advanced facial recognition system based on a hybrid deep learning framework that integrates VGG19 and ResNet-101 for robust feature extraction. VGG19 captures fine-grained facial details such as edges and textures, while ResNet-101 extracts deeper semantic features using residual learning. The combined feature representation enhances the system’s ability to distinguish individuals under complex conditions. Furthermore, a Support Vector Machine (SVM) classifier is employed to improve decision boundaries and classification accuracy in high-dimensional feature space. The proposed system is designed for real-time applications such as criminal identification and missing person detection using surveillance data. Experimental results demonstrate improved accuracy, strong generalization, and reliable performance when compared to traditional CNN-based approaches. The system effectively handles real-world variations and provides a scalable solution for modern law enforcement operations.