PrivFace-AI: A Federated Learning-Based Privacy-Preserving Face Recognition Framework for Unconstrained Environments

Abstract

Face recognition in unconstrained environments remains a challenge due to variations of illumination, pose, expression and partial occlusion while large scale biometric deployment raises significant privacy concern with regards to processing sensitive facial data. This paper presents PrivFace-AI which is a framework for private face recognition using federated learning to securely perform biometric identification without transferring raw facial data to centralized servers. PrivFace-AI combines deep embeddings extracted from facial embedding using FaceNet; cosine similarity matching; decentralized model aggregation via FedAvg and liveness detection which improves robustness against spoofing attacks under real world operating conditions. A complete web-based implementation coupled with optional embedded hardware interaction was developed to validate practical deployment of the framework in intelligent access control scenarios. Experimental results obtained on LFW, KinectFace and custom real world datasets demonstrate 97.8% recognition accuracy in real-time response capability confirming the effectiveness of PrivFace-AI in combining privacy preservation, recognition robustness and operational feasibility under unconstrained environment.