Title: Computer Vision-Based Campus Access Control and Monitoring System: A case study at Ruaha catholic university
Authors: Eziberi Chuma, Paulo KItangita,Abdulkadir Mohammed,Christina Ganagwa,Faston Kataita,Hamisi Mihezo,Dayness Urio and Danny Mfungo
Volume: 10
Issue: 4
Pages: 87-92
Publication Date: 2026/04/28
Abstract:
this paper presents Campus security breaches pose significant risks to student safety and institutional assets, particularly through unauthorized access via fake or borrowed identification cards. Traditional manual verification methods visual inspection of ID cards and logbook sign-ins-are time-consuming, subjective, prone to human error, and difficult to scale across multiple entry points. This leads to delayed response to security incidents, overcrowded entry gates during peak hours, and lack of actionable data for security management. Advances in computer vision and deep learning now enable automated facial recognition systems for rapid and accurate identity verification. Cameras capture faces at entry points, and lightweight models provide instant authentication results (identity match, confidence score, access decision) either on edge devices or via campus servers.