The rapid progress in facial recognition technology has broadened its use across multiple sectors, with law enforcement and public safety being among the primary areas of impact. This study presents an innovative framework for identifying criminals and missing persons by integrating two cutting-edge technologies: FaceNet and MTCNN. FaceNet, a deep learning-based model, produces high-dimensional facial embeddings that capture unique facial features consistently across various conditions, while MTCNN performs real-time face detection, isolating facial regions accurately to improve identification precision. The combined application of FaceNet and MTCNN addresses common challenges in facial identification, such as changes in lighting, pose, and expression, providing law enforcement with a robust tool to expedite investigations and locate missing individuals. Through testing on diverse datasets, this study assesses the system's effectiveness, focusing on practical applicability and examining ethical concerns, privacy protections, and potential societal impacts. This research contributes to the ongoing discussion on using advanced technologies responsibly to enhance public safety and support law enforcement efforts
Nayana et al. (Tue,) studied this question.