A HYBRID FRAMEWORK FOR MASK-RESILIENT FACE RECOGNITION AND ANTI-SPOOFING TO ENHANCE SECURE VOTER AUTHENTICATION

Abstract

Biometric authentication systems employed in Nigeria’s electoral process continue to encounter significant challenges, particularly in addressing impersonation, facial occlusion, and presentation attacks. Although technologies such as the Bimodal Voter Accreditation System (BVAS) have enhanced electoral transparency and credibility, concerns regarding system reliability and susceptibility to spoofing attacks persist. To mitigate these limitations, this study proposes a hybrid framework that integrates mask-resilient face recognition with anti-spoofing mechanisms for secure voter authentication. The framework is based on a deep convolutional neural network (CNN) trained on a dataset comprising 1,478 facial images categorized into real, masked, and spoof classes. Standard preprocessing procedures, including face detection, image resizing to 128 × 128 pixels, and normalization, were applied to ensure input consistency and optimize model performance. Experimental results indicate that the proposed model achieves 97.3% classification accuracy, demonstrating its ability to effectively distinguish among real, masked, and spoof facial inputs. Furthermore, biometric evaluation metrics, namely the Attack Presentation Classification Error Rate (APCER), Bona fide Presentation Classification Error Rate (BPCER), and Average Classification Error Rate (ACER), confirm the model’s effectiveness in detecting spoofing attempts while maintaining acceptable performance for legitimate users. In conclusion, the findings suggest that the proposed framework offers strong potential for real-world biometric authentication applications. It provides an efficient, integrated solution for enhancing security in electoral systems and can be extended to other security-critical domains.