Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

Fuzzy biometrics based signatures

Abstract: The present invention relates to a method and a device of verifying the validity a digital signature based on biometric data. A basic idea of the invention is that a verifier attains a first biometric template of the individual to be verified, for instance by having the individual provide her fingerprint via an appropriate sensor device. Then, the verifier receives a digital signature and a second biometric template. The verifier then verifies the digital signature by means of using either the first or the second biometric template as a public key. The attained (first) biometric template of the individual is compared with the received (second) biometric template associated with the signature and if a match occurs, the verifier can be confident that the digital signature and the associated (second) biometric template have not been manipulated by an attacker for impersonation purposes.

Online signature recognition

Abstract: In this paper, online signature recognition is examined by using K Nearest Neighborhood (KNN) method. The signatures are collected by an Android application which can extract the dynamic and spatial information of the signatures. In this frame, a signature database is consisting of a total of 120 signatures taken from 12 different person. The purpose of this paper, is to obtain high performance with a few training signatures. Also, the performance of signature recognition is investigated by different distance measurement methods in K Nearest Neighborhood.

Improving signature-based biometric cryptosystems using Cascaded Signature Verification-Fuzzy Vault (SV-FV) approach

Abstract: 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 signaturebased 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.

Real time implementation of HMM speech recognition for telecommunications applications

Abstract: This paper describes an implementation of connected word recognition using commercially available parallel processing DSPs. The recognition system uses continuous density HMMs for speaker independent recognition over a public switched network. It describes how the computationally intensive functions can be optimised for efficient real time implementation. Results of recognition accuracy are presented for a difficult task of connected digit recognition with data from a live operator environment and an isolated digit recognition task. >