A Survey on Privacy-Preserving Face Recognition: Techniques, Challenges and Future Directions

Abstract: Face recognition (FR) is now widely deployed across authentication, surveillance, and identity management systems. However, because facial data is inherently sensitive, storing, transmitting, and processing it introduces severe privacy and security risks. Cloud-hosted FR architectures, in particular, leave raw facial images and deep feature embeddings vulnerable to data breaches, adversarial manipulation, and unauthorized inference. To address these vulnerabilities, this survey delivers a systematic review of privacy-preserving face recognition (PPFR) techniques published between 2019 and 2026. Analyzing primary studies alongside more than related works, we organize current research into a five-part taxonomy: cryptographic approaches (fully homomorphic, selective, and searchable encryption), distributed and perturbation methods (federated learning and GAN-based anonymization), hardware-level protection (lensless imaging), adversarial robustness (attack and defense methods), and domain-specific applications (blockchain, IoT, and e-voting). For each category, we unpack the core methodology, benchmark datasets, reported accuracy, and the operational tradeoffs among privacy, accuracy, and computational overhead. Our analysis highlights key trade-offs: homomorphic encryption retains near-perfect recognition accuracy but incurs high computational overhead; federated learning and lensless imaging deliver stronger privacy at a moderate cost to accuracy; and adversarial robustness remains a widespread open gap across most PPFR designs. We conclude by outlining key open challenges and future research directions.

Keywords: Privacy-Preserving Face Recognition, Homomorphic Encryption, Federated Learning, Adversarial Robustness, Biometric Template Protection, Lensless Imaging.