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Signature recognition

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


Papers
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Journal Article
TL;DR: A deep learning approach for offline signature verification to prevent the fraud signatures by fake peoples is presented and deep learning with the help of Convolution Neural Network is done.
Abstract: Now a day’s signature is becomes a most important biometric authentication technique. In banks or at the other necessary documents, signature plays an important role to authenticate the person. In this technique, we are going to present a deep learning approach for offline signature verification to prevent the fraud signatures by fake peoples. We are going to do deep learning with the help of Convolution Neural Network (CNN). In this study, we are going to collect dataset of different signatures from the different angles. Signature is taken as an input in the form of image. For signature recognition, it is important to make structural and some geometrical calculation getting to extract special features from the signatures then we train a man-made neural network on these features from different signers. Finally, the extracted features from the tested signature are compared with the previously trained features and that we know the signer.
Proceedings ArticleDOI
31 Dec 2009
TL;DR: In this paper, a new fingerprint recognition method based on the bases of graph model has been proposed, which is based on feature vectors derived from their physiological and behavioral characteristic, which can be used for automatic recognition of individuals based on a feature vector.
Abstract: Biometric recognition refers to an automatic recognition of individuals based on a feature vectors derived from their physiological and behavioral characteristic. In this paper proposed a new fingerprint recognition method on the bases of graph model.
Journal ArticleDOI
TL;DR: This paper attempts to improve the performance of signature based authentication system by cascading orthogonal transforms using WACOM digital pen tablet, and confirms the advantages of the cascaded approach over unimodal approaches.
Abstract: Biometrics methods of human identification have gained much attention recently, mainly because they easily deal with most problems of traditional identification, since users are identified by who they are, not by something they have to remember or carry with them. This paper attempts to improve the performance of signature based authentication system by cascading orthogonal transforms. The signatures are acquired using WACOM digital pen tablet and then features are extracted. Our experimental results on the image data set from 75 users, confirm the advantages of our cascaded approach over unimodal approaches. It should be noted that a distinct advantage of the proposed system is that it does not require multiple signature samples for training.
Proceedings Article
27 Sep 2012
TL;DR: The hypothesis is that by sub-dividing a signature image, information richness within sub-divisions can be exploited by weighting grid zones, and a best practice framework for the utilisation of sub-regions-of-interest within biometric signature images is developed to enable an optimisation of systems.
Abstract: The identification and subsequent utilisation of regions of interest within biometric sample images can provide useful information that can benefit recognition performance . If a specific area of a biometric sample contains an enhanced quantity of feature information then these regions-of-specific-interest can be exploited for example in terms of processing and information weighting. Also, if intra-area stability/feature repeatability can be obtained apriori this information can be used to enhance biometric systems. The objective of the work documented in this paper is to develop a best practice framework for the utilisation of sub-regions-of-interest within biometric signature images to enable an optimisation of systems. Our hypothesis is that by sub-dividing a signature image, information richness within sub-divisions can be exploited by weighting grid zones. Signature images were divided using 14 experimental template patterns. Using the GPDS-960 off-line signature corpus, the verification performance achieved using each weighted method was compared against a non-gridded baseline implementation. Significant improvements were noted for a number of the defined grid zones indicting the potential for the approach.

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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202310
202219
202122
202028
201925
201832