AI-Based Facial Recognition for Secure Prisoner Attendance Management

Accurate monitoring of the attendance of inmates within correctional institutions plays an important role in security and efficiency. Manual methods are inefficient, prone to errors, and subject to impersonation, thus calling for the use of an automated, intelligent solution. This paper seeks to introduce an automated method of efficiently and effectively monitoring the attendance of prisoners using computer-based facial recognition technology. This method will be more accurate while easing the burden on correctional officers.

The system consists of various components that work in harmony to achieve the objectives of face detection, feature extraction, and recognition. The system is capable of detecting face landmarks such as the eyes, nose, and mouth to achieve the objectives of feature extraction. The system is capable of generating a numerical vector to represent the face characteristics of the individual. The system is capable of capturing images to achieve the objectives of identification through processing of the images to compare with the registered individuals. The system is fast, does not require a special server, is capable of preventing fraud in attendance, and is capable of serving a large number of users, ensuring security. The improvements that can be made to the system in the future include improving performance in low lighting and with visual obstructions, improving the recognition capabilities of the system through better computing facilities. The system is capable of integrating with existing prison management systems; hence, it has the potential to serve a wider number of users.