Improving Signature-Based Biometric Cryptosystems Using Cascaded Signature Verification-Fuzzy Vault (SV-FV) Approach

Biometric cryptosystems have been applied to secure secret keys for encryption and digital signatures by means of biometric traits, e.g., Fingerprint, face, etc., where the fuzzy vault (FV) mechanism has been extensively employed. Recently, the authors proposed a FV system based on the offline signature images, so that digitized documents can be secured with the embedded handwritten signatures. However, the FV design concerns mostly with alleviating biometric variability with less focusing on its power in discriminating forgeries. Accordingly, the decoding accuracy of implementations is below the level required in practical banking transactions. On the other hand, signature verification (SV) systems have shown higher accuracy in discriminating forgeries. In this paper, accuracy of signature-based biometric cryptosystems is enhanced by cascading SV and FV modules. Signature samples are first verified by the SV module. Then, only verified samples are processed by FV decoders for unlocking cryptographic keys. Hence, the upper limit of the false accept rate is determined by the more accurate SV module. Simulation results obtained with the Brazilian signature database indicate the viability of the proposed approach. Cascaded SV-FV system increases decoding accuracy by about 35% compared to the pure FV systems.