A Study of the Variation in the Dimensions of Genuine Signatures

Evett, I.W.; Totty, R.N.

doi:10.1016/s0015-7368(85)72393-6

Cited by 25 publications

(10 citation statements)

References 1 publication

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“…But depending on practical situations and user requirement we can set preferences for accuracy. [14], [5], [6] While comparing our system with the other techniques in Statistical class like Moment based approach or combination feature and moments or the techniques using Euclidian distance, the accuracy achieved is definitely higher in our system. [9], [10] …”

Section: Comparison With Other Techniquesmentioning

confidence: 86%

“…Although the test signature seems to be authentic, it is actually a forgery. [5], [8], [9] [10] et al…”

Section: Off-line Signature Verificationmentioning

confidence: 99%

“…If the dissimilarity between the test and reference set signatures is above a certain threshold, the user is rejected, otherwise accepted. [3], [5] The dissimilarity between two signatures can be established in two ways: if each time a signature is presented to the system, equal number of features is being extracted from that signature, some sort of distance (ex. Euclidian distance) can be used to compare these two signatures.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately no public signature database of either type is available, which makes it difficult to compare existing signature verification systems. [6], [5], [11] …”

Section: Introductionmentioning

confidence: 99%

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A Colour Code Algorithm For Signature Recognition

Kulkarni¹

2007

ELCVIA

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The paper "A Colour Code Algorithm for Signature Recognition" accounts an image processing application where any user can verify signature instantly. The system deals with a Colour code algorithm, which is used to recognize the signature.The paper deals with the recognition of the signature, as human operator generally make the work of signature recognition. Hence the algorithm simulates human behavior, to achieve perfection and skill through AI. The logic that decides the extent of validity of the signature must implement Artificial Intelligence Pattern recognition is the science that concerns the description or classification of measurements, usually based on underlying model. Since most pattern recognition tasks are first done by humans and automated later, the most fruitful source of features has been to asked the people who classify the objects how they tell them a part . Signatures are a behavioural biometric that change over a period of time and are influenced by physical and emotional conditions of a subject. In addition to the general shape of the signed nameThe algorithm is tested on various operating systems & we find that it works very well & satisfactory. While implementing the recognition process, we have used quite simpler way. At this stage we are getting accuracy up to about 80% to 90%.

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Section: Comparison With Other Techniquesmentioning

confidence: 86%

“…Although the test signature seems to be authentic, it is actually a forgery. [5], [8], [9] [10] et al…”

Section: Off-line Signature Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Unfortunately no public signature database of either type is available, which makes it difficult to compare existing signature verification systems. [6], [5], [11] …”

Section: Introductionmentioning

confidence: 99%

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A Colour Code Algorithm For Signature Recognition

Kulkarni¹

2007

ELCVIA

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“…The proposed system of signature verification works only for offline signatures to reduce the complexity of the system architecture and to avoid the need of specialized hardware for collection of online signatures. There has been a long line of works in automated signature verification [1,2,3]. Still the verification accuracy of the present signature verification systems is not very impressive and the systems are not very efficient in the presence of noise for offline signature verification.…”

Section: Introductionmentioning

confidence: 98%

On Analysis of Multi-dimensional Features for Signature Verification

Mahmud

Rahman

International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on In

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This paper aims to verify offline signatures using improved feature analysis and artificial neural network. Feature analyzer can reduce the large domain of feature space and extract invariable information. We incorporated different features from multi-dimensional feature analysis perspective. For verification from extracted features, we used neural network classifier. Instead of using feed forward neural network, multiple feed forward neural networks are used which are trained in the form of ensemble. Using such ensemble makes the system more general than a regular single neural network based system. Use of resilient back propagation for each neural network training, provides faster recognition. Using cross validation techniques, we performed significant amount of testing. Experimental evaluation of the signature verifier is reported.

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Signature Examination

2015

Foundations of Forensic Document Analysis

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A Study of the Variation in the Dimensions of Genuine Signatures

Cited by 25 publications

References 1 publication

A Colour Code Algorithm For Signature Recognition

A Colour Code Algorithm For Signature Recognition

On Analysis of Multi-dimensional Features for Signature Verification

Signature Examination

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