Abstract

Mobile payment systems face significant trust challenges within the Nigerian population, stemming from perceived risks, system complexity, and concerns about reliability. In response, the 2011 Cashless Policy enacted by the Central Bank of Nigeria (CBN) has continued to drive the development of safer and more user-friendly mobile payment platforms. Common security mechanisms include personal identification numbers (PINs), biometric verification, and facial recognition. Integrating these modes provides enhanced security and addresses key gaps in Nigeria’s cybersecurity landscape. This research presents the development of a multi-level security model for mobile payment systems, named PINAF, designed to address these challenges. The PINAF application encompasses user enrollment, authentication, and payment processing. Face detection was implemented using the Viola-Jones algorithm, facial recognition was performed using a Support Vector Machine (SVM), and user data was encrypted using the Rivest-Shamir-Adleman (RSA) algorithm. Evaluation results showed that the Viola-Jones + SVM approach achieved 90% accuracy, 92.3% recall, and an F1-score of 0.90, outperforming existing facial recognition models. The proposed model demonstrates a significant improvement in the security and reliability of mobile payment systems in Nigeria.

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