Privacy-preserving Preselection for Face Identification Based on Packing
This work addresses privacy concerns in face identification systems by improving retrieval efficiency for large ciphertext template libraries, though it is incremental as it builds on existing ciphertext domain methods.
The paper tackles the challenge of slow face retrieval in ciphertext domain systems by proposing PFIP, a scheme that uses preselection and packing to reduce computational overhead, achieving a 100% hit rate and retrieving 1,000 templates in 300 milliseconds with a 50x efficiency improvement.
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.