Privacy-preserving Preselection for Face Identification Based on Packing

TL;DR

This paper introduces PFIP, a novel privacy-preserving face retrieval scheme in the ciphertext domain that significantly improves efficiency while maintaining high accuracy, enabling faster face identification in large encrypted databases.

Contribution

The paper presents PFIP, a new preselection and packing method that reduces computational time in encrypted face retrieval without sacrificing recognition accuracy.

Findings

Achieves 100% hit rate on LFW and CASIA datasets.

Retrieves 1,000 ciphertext templates within 300 milliseconds.

Offers nearly 50x improvement in retrieval efficiency.

Abstract

Face identification systems operating in the ciphertext domain have garnered significant attention due to increasing privacy concerns and the potential recovery of original facial data. However, as the size of ciphertext template libraries grows, the face retrieval process becomes progressively more time-intensive. To address this challenge, we propose a novel and efficient scheme for face retrieval in the ciphertext domain, termed Privacy-Preserving Preselection for Face Identification Based on Packing (PFIP). PFIP incorporates an innovative preselection mechanism to reduce computational overhead and a packing module to enhance the flexibility of biometric systems during the enrollment stage. Extensive experiments conducted on the LFW and CASIA datasets demonstrate that PFIP preserves the accuracy of the original face recognition model, achieving a 100% hit rate while retrieving 1,000 ciphertext face templates within 300 milliseconds. Compared to existing approaches, PFIP achieves a nearly 50x improvement in retrieval efficiency.