Theme parks, concerts, and other large-crowd events typically have long queues that lead to visitor dissatisfaction. The dissatisfaction is not only due to the long waiting time, but also due to inaccurate estimates of wait times and suspected line cutting by other visitors. The purpose of this project was to develop and test prototype modules for an automated queuing system using facial recognition. The developed prototype utilized facial recognition to process individuals, automate wait time calculations, and identify people at the wrong position in the queue. The design involved three stages in the queue: data collection, data validation, and resolution. Entry timestamps were collected as individuals entered the queue, verified at an intermediate point, and exit timestamps were recorded to calculate the time spent in the queue. The system detected line-cutting and tracked individuals in the queue. Testing results showed promise toward improvements in accuracy over manual tracking methods. Further development and testing are necessary to refine the system and ensure reliability in diverse operational environments. Such an automated queuing system could improve the user experience by reducing frustration while in line.