Quantized random projections of SIFT features for cancelable fingerprints.

Biometric recognition, particularly fingerprint, has been widely adopted in many civil and military applications. However, security concerns have arisen regarding the protection of the saved biometric templates. If the fingerprints database is compromised, the person can no longer use his/her registered fingerprint for authentication. Cancelable biometrics have been used to overcome this issue, by transforming fingerprint templates to a secure representation before saving them on the database. The identity verification is performed in the transformed domain, and a new transform is assigned to a user if his/her biometric data is compromised. In this context, we propose a unique cancelable fingerprint scheme based on the extraction of Scale Invariant Feature Transform (SIFT) features from fingerprint minutiae positions. To provide cancelability, SIFT features are transformed by applying user-specific random projections, followed by quantization to ensure irreversibility. We successfully achieved a zero Equal Error Rate (EER) for the Fingerprint Verification Competition (FVC) 2002 DB1 benchmark in the different-key scenario, and 1.78 EER in the stolen-key scenario. We compare our results with state-of-the-art methods based on the EER metric in the stolen-key scenario. Genuine and impostor distributions have also been used for performance and security assessment of the proposed method. Moreover, we demonstrate the robustness of the proposed approach against brute force attacks. [ABSTRACT FROM AUTHOR]