A Survey of Face Detection Techniques for Secure Access in Smart Ticketing Systems

Abstract

Facial recognition has emerged as a pivotal technology for identity verification in modern applications, especially in security-sensitive environments. This survey paper explores various face detection techniques with a focus on their suitability for integration into smart ticketing systems such as Gaitpass, a proposed contactless metro entry solution that authenticates users based on facial features. The goal is to understand and evaluate the strengths, limitations, and performance metrics of state-of-the-art algorithms—ranging from classical approaches like Haar cascades to advanced models such as FaceBoxes, BlazeFace, and ArcFace. By analyzing ten recent research papers, this study reviews the current landscape of face detection systems across different parameters including accuracy, speed, real-time capability, and robustness under occlusion or poor lighting. A comprehensive comparison is presented in tabular form, followed by identified research gaps and future directions for developing efficient, scalable, and privacy-conscious systems suitable for public transport environments. In this survey, it serves as a reference point for researchers and developers aiming to implement face detection in real-world access control systems.