Real-time Multi-source Facial Recognition System for Criminal Identification in Diverse Environment
Keywords:
Criminal identification, Intelligent video surveillance, Law enforcement tools, Public safety technology, Semi-regulated environments, Social media image analysis, Surveillance systems, Unregulated environmentsAbstract
Facial recognition technology has emerged as a vital tool for real-time crime detection and prevention, demonstrating remarkable advancements in recent years. Existing algorithms primarily function within regulated environments, limiting their applicability in broader contexts. This study presents a real-time facial recognition algorithm that operates effectively across regulated, semi-regulated, and unregulated environments. A novel module was integrated into the system to receive and process images from social media platforms and institutional data hubs. Data analysis used descriptive statistics, including frequency distributions and percentages, while simulation techniques evaluated the system’s performance in terms of effectiveness, efficiency, and precision. Results revealed that the proposed algorithm achieved high levels of efficiency (short processing time) and precision (accurate image detection). The study concluded that the algorithm’s ability to utilize social media and data hub image reservoirs enables efficient and precise tracking of criminal faces across diverse environments. Consequently, the study recommends implementing the algorithm in semi-regulated and unregulated settings while emphasizing the need for professional training of CCTV operators and data analysts, alongside adherence to ethical and legal standards.
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