Topic
Signature recognition
About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.
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08 Dec 2011
TL;DR: The introduction of DBS as another variant of the legislatively supported electronic signature creates a new, considerably simpler means of signing electronic documents and eliminates the major complication in existing authentication of the documents and their storage in electronic form.
Abstract: This paper examines the current status in the field of securing electronic documents through cryptographic tools such as electronic signatures, electronic mark and time stamp, especially in terms of their long-term storage and authentication. The authors propose a solution that removes the limitation in the use of certificates, which are constrained by the duration of validity. This is the use of dynamic biometric signature (DBS) as a natural and easily accessible tool for one-factor authentication. In fact, this method utilizes the analysis of several factors, however, to the user the method appears only as a one-factor method. The introduction of DBS as another variant of the legislatively supported electronic signature creates a new, considerably simpler means of signing electronic documents and eliminates the major complication in existing authentication of the documents and their storage in electronic form. Dynamic signature contains biometric information that reflects the characteristics of the signing person, i.e. the habits and behaviors. These properties represent a biometric footprint, which is unique to each individual and cannot be reproduced by counterfeiters. Verification of a person using his signature is one of the most natural biometric methods, since we are accustomed to verify everything with signature. The Dynamic biometric signature technology can provide the same level of security as is in the case of electronic signatures employing certificate-based cryptographic methods.
14 citations
22 Mar 2011
TL;DR: Experimental results affirm that a weighted sum based fusion achieves excellent recognition performances, which out performs both single biometric systems.
Abstract: Single modality biometric recognition system is often not able to meet the desired system performance requirements. Several studies have shown that multimodal biometric identification systems improve the recognition accuracy and allow performances that are required for many security applications. In this paper, we have developed a multimodal biometric recognition system which combines two modalities: face and fingerprint. For face trait, we build features based on Gabor Wavelet Networks (GWNs), while Local Binary Patterns (LBP) is used for fingerprint trait. Experimental results affirm that a weighted sum based fusion achieves excellent recognition performances, which out performs both single biometric systems.
14 citations
15 Nov 2011
TL;DR: A method for online signature verification treated as a two-class pattern recognition problem based on the acceleration signals obtained from signing sessions using a special pen device and represented the results in terms of a distance metric.
Abstract: Here we present a method for online signature verification treated as a two-class pattern recognition problem. The method is based on the acceleration signals obtained from signing sessions using a special pen device. We applied a DTW (dynamic time warping) metric to measure any dissimilarity between the acceleration signals and represented our results in terms of a distance metric.
14 citations
23 Aug 2004
TL;DR: This work investigates three different rejection strategies for offline handwritten sentence recognition implemented as a postprocessing step of a hidden Markov model based text recognition system and are based on confidence measures derived from a list of candidate sentences produced by the recognizer.
Abstract: This work investigates three different rejection strategies for offline handwritten sentence recognition. The rejection strategies are implemented as a postprocessing step of a hidden Markov model based text recognition system and are based on confidence measures derived from a list of candidate sentences produced by the recognizer. The better performing confidence measures make use of the fact that the recognizer integrates a word bigram language model. Experimental results on extracted sentences from the IAM database validate the effectiveness of the proposed rejection strategies.
14 citations
Patent•
05 Nov 2010
TL;DR: In this paper, a system and method for generating an encryption key using physical characteristics of a biometric sample is described, where the biometric feature(s) from a sample are analyzed to generate a feature vector.
Abstract: A system and method for generating an encryption key using physical characteristics of a biometric sample is described. In one embodiment, the biometric feature(s) from a sample are analyzed to generate a feature vector. After discretizing the feature(s), the resultant feature vector is translated into a bit vector. The bit vector is the secure biometric key that results from the biometric(s). The secure biometric key is used to generate at least one cryptographic key. A similar process is used to access the cryptographic key secured by the secure biometric key. If the access biometric key matches the secure biometric key, the cryptographic key is revealed and access is allowed. In another embodiment, if the access biometric key does not match the secure biometric key a camouflaging process is used to provide an unauthorized user a bogus secure biometric key indistinguishable from the correct secure biometric key.
14 citations