Signature based Fuzzy Vaults with Boosted Feature Selection

Handwritten signatures are commonly employed in many financial and forensic processes, and secure offline signature verification systems (SV) are required to automate such processes. In this context, bio-cryptography systems based on the handwritten signatures may be considered for enhance security. This paper presents a bio-cryptography system that constructs Fuzzy Vaults (FVs) based on the offline signature images. Boosting Feature Selection is employed to select features while training weak classifiers of offline SV systems. The indexes of selected features correspond to the most stable and discriminant features from a user´s signature images, and are used to encode user-specific FVs. A password is employed as a second authentication measure, to further enhance system security. During authentication, a user provides both the signature and the password to decode the FV and decouple his private key. If the FV is correctly decoded, the user is authenticated by the verification system. The proposed FV implementation alleviates the security vulnerabilities of the classical SV systems like template security, repudiation, irrevocability, and bypassing the classification decision. Moreover, simulations performed on a real-world signature verification database (with random, simple, and skilled forgeries) indicate security guarantees against stolen authentication measures. While compromised signatures or passwords lead to complete fail (FAR = 100%) of the classical SV or password protected cryptography systems respectively, compromised signatures lead to FAR of 0.1%, and compromised passwords leads to FAR of 15% with the proposed system.