Design of a Mobile‑Based Attendance System Using Facial Recognition: A Case Study of Plateau State Polytechnic, Barkin Ladi, Barkin Ladi LGA
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Background of the Study
In recent years, the integration of biometric technologies in academic administration has revolutionized attendance management. At Plateau State Polytechnic, Barkin Ladi, traditional attendance methods such as manual registers have proven inefficient and prone to errors. The proposed mobile‑based attendance system using facial recognition leverages advanced computer vision and machine learning techniques to accurately record student presence in real time. This system utilizes smartphone cameras to capture facial images and matches them against a pre‑registered database, ensuring a seamless, contactless, and rapid verification process (Adeyemi, 2023; Okafor, 2024). By automating attendance capture, the system minimizes proxy attendance and reduces administrative burden, allowing staff to focus on core academic functions. The mobile application is designed with an intuitive user interface that facilitates easy registration and real‑time updates. Integration with the institution’s database ensures that attendance data is securely stored and readily available for analysis, thereby enabling prompt interventions in cases of chronic absenteeism. Furthermore, the system can generate comprehensive attendance reports, which are useful for evaluating student engagement and informing policy decisions. Continuous learning algorithms enhance the system’s accuracy by adapting to variations in lighting and facial expressions. However, challenges such as network reliability, data privacy concerns, and ensuring system scalability must be addressed to ensure effective deployment. Pilot studies in similar academic settings have indicated that facial recognition technologies can dramatically improve attendance accuracy and operational efficiency (Chinwe, 2025). Overall, the development of a mobile‑based attendance system using facial recognition represents a significant advancement in academic administration that aligns with global trends in digital transformation, aiming to enhance institutional productivity and student accountability.
Statement of the Problem
Plateau State Polytechnic currently relies on outdated manual attendance methods that are inefficient and error‑prone, resulting in inaccurate records and increased administrative workload. The manual system is susceptible to proxy attendance and misreporting, which negatively impact academic planning and student performance evaluations. Although technological solutions exist, the institution has yet to implement a robust, automated system capable of real‑time attendance monitoring using facial recognition. Technical challenges, including inconsistent network connectivity, data storage, and ensuring the accuracy of facial matching algorithms, hinder the adoption of modern biometric systems. Furthermore, concerns over data privacy and the potential misuse of biometric data have created reluctance among stakeholders. These issues lead to delays in verifying attendance, diminished accountability, and reduced trust in the administrative process. This study aims to bridge the gap between traditional attendance methods and modern digital solutions by developing and evaluating a mobile‑based system that uses facial recognition. The research will assess system performance, identify technical and operational challenges, and propose strategic solutions to ensure secure, reliable, and efficient attendance management. Addressing these challenges is crucial for improving academic integrity and administrative efficiency at Plateau State Polytechnic (Okafor, 2024).
Objectives of the Study
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To design and implement a mobile‑based attendance system using facial recognition technology.
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To evaluate the system’s performance in terms of accuracy, efficiency, and security.
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To propose strategies for overcoming network and data privacy challenges.
Research Questions
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How does the facial recognition attendance system improve accuracy compared to manual methods?
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What technical challenges hinder the system’s integration and performance?
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Which measures can enhance data security and user acceptance?
Significance of the Study
This study is significant as it introduces a cutting‑edge, mobile‑based attendance system that harnesses facial recognition technology to enhance accuracy and efficiency at Plateau State Polytechnic. By reducing proxy attendance and administrative errors, the system promises to improve academic accountability and resource management. The findings will provide valuable insights for educational institutions seeking to implement biometric solutions for streamlined attendance tracking (Adeyemi, 2023).
Scope and Limitations of the Study
This study is limited to the design and implementation of a mobile‑based attendance system using facial recognition at Plateau State Polytechnic, Barkin Ladi, Barkin Ladi LGA.
Definitions of Terms
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Facial Recognition: An AI-driven technology that identifies individuals by analyzing facial features.
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Mobile-Based System: An application designed for smartphones and tablets.
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Attendance Management: The process of recording and verifying student presence in academic settings.
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