Develop Deep Learning-Based Face Recognition Model for Masked and Unmasked Faces

Abstract

Traditional facial recognition systems are no longer as effective due to the widespread use of face masks. To overcome this difficulty, the study suggests a facial recognition (FR) system based on a unified framework specifically designed for partial facial occlusion scenarios that can reliably identify people wearing or not wearing face masks. It is built on the Inception ResNet V1 deep learning model and was trained using the Real-World Masked Face Dataset (RMFD) with 525 subjects. The proposed system outperforms existing methods like FaceNet, achieving a test accuracy of 98.3% through rigorous subject-level cross-validation, while maintaining real-time performance at 45 FPS with optimized model size of 96MB. The solution, which is optimized for real-time deployment, can be used for public health monitoring, access control, and security verification. In both masked and unmasked faces, experimental results show strong generalization and consistent accuracy, allowing for a smooth integration into safety-critical applications without sacrificing effectiveness.