A Comparative Study of Face Recognition Algorithms for Investigating Watchlist

Current immigration checkpoint procedures are often hindered by various delays, including lengthy manual inspections, document verification, and identity confirmation processes. These inefficiencies not only inconvenience travelers but also strain border security resources. As global travel increases, the need for more efficient and accurate immigration processing has become increasingly critical. This paper explores the potential of a new facial recognition system to streamline these procedures, reduce wait times, and enhance overall security. We implemented deep learning models for recognizing facial features through two different algorithms and compared their performance. A final model with the best performance in terms of speed and accuracy was selected. Through this research, we hope to alleviate the delays currently faced at immigration checkpoints, offering a faster and more reliable alternative to traditional methods for significant parts of the procedure.