Improving Law Enforcement Operations: Integrating Machine Learning and Facial Recognition Technology into Smart Helmets. رسالة ماجستير

Abstract

This study aimed to design, develop, and evaluate a prototype smart helmet integrated with real-time facial recognition technology to enhance law enforcement operations. Conducted through a sequential exploratory mixed-methods design, the research combined quantitative experimental testing with qualitative user-centered evaluation. The prototype was built using an NVIDIA Jetson Xavier NX module, a high-quality camera, and a cloud-connected facial recognition pipeline based on an optimized FaceNet model. Quantitative testing in controlled laboratory and simulated field environments at a police training academy revealed that the system achieved a mean F1-score of 0.930 on benchmark datasets, with an end-to-end latency of 148 milliseconds, demonstrating technical feasibility for real-time use. However, a significant performance disparity was identified, with lower accuracy rates for female subjects (F1-score: 0.901) and individuals with darker skin tones (F1-score for V–VI: 0.882), confirming the presence of algorithmic bias. System performance also degraded under low-light conditions (<10 lux), where the F1-score dropped to 0.71. Qualitative data from semi-structured interviews and focus groups with officers (N = 30) highlighted key themes: enhanced situational awareness was valued, but concerns about cognitive overload, ergonomic discomfort, and profound ethical implications—including privacy risks, mission creep, and community trust—were predominant. Officers emphasized that technical reliability alone was insufficient for trust, which was easily eroded by errors, and called for stringent governance. v The study concludes that while the smart helmet prototype is a technically viable tool that offers superior speed compared to traditional manual identification, its deployment must be preconditioned on rigorous bias mitigation, strict regulatory frameworks governing use, transparent data policies, and comprehensive officer training. The research contributes a holistic, evidence-based framework for the responsible development of AI-powered wearable technologies in policing.