Optimizing Facial Recognition Systems for Large Populations: Insights from Public Target Detection Methods - Alkhafaji, Safaa Hakeem Obaid; Yaghoub Farjami; Rouhollah Dianat (International Journal of Computational and Experimental Science and Engineering, 2025)
Inspired by findings in the public target detection regime, this study proposes to improve facial recognition system performance in large population by drawing insight and blending practical methods. The research goals consist of developing and testing optimized procedures to increase accuracy and productivity. We assessed the performance metrics of facial recognition systems using mixed methods through experimental setups, simulations and user studies. When the public target detection methods are applied, significant improvements in recognition accuracy and the system efficiency are observed. The implications of this research are related to technological practices and public safety in terms of being able to create a more efficient and reliable facial recognition technology for public environments. However, because this study brings together some of the theoretical advancements in facial recognition technology with its practical applications while also ensuring that this technology is both effective and ethical, this work contributes uniquely to the field of facial recognition technology and public safety.