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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 ArticleDOI
TL;DR: In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit.
Abstract: Various techniques are already introduced for personal identification and verification based on different types of biometrics which can be physiological or behavioral. Signatures lies in the category of behavioral biometric which can distort or changed with course of time. Signatures are considered to be most promising authentication method in all legal and financial documents. It is necessary to verify signers and their respective signatures. This paper presents an Offline Signature recognition and verification system(SRVS). In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit.

6 citations

Proceedings ArticleDOI
01 Sep 2012
TL;DR: A system exploiting polynomial classifiers, typically employed in identification scenarios, for the case of user verification based on on-line signature guarantees high verification performance while requiring low complexity and low storage capacity for the employed users' templates.
Abstract: In this paper we propose a system exploiting polynomial classifiers, typically employed in identification scenarios, for the case of user verification based on on-line signature. In order to accomplish this task, a novel strategy for generating synthetic classes of signature features is proposed. The proposed system guarantees high verification performance while requiring low complexity and low storage capacity for the employed users' templates. Experimental tests conducted over the public MCYT database show the effectiveness of the proposed approach.

6 citations

01 Jan 2010
TL;DR: This work develops methods that enable recognition using 2D ear images, and explores the growing field of ensemble biometrics, which subdivide a biometric feature into parts, and combine the results of several parts to yield recognition results.
Abstract: In this work, we explore hard and soft biometric systems. Hard biometrics are features that are used to uniquely identify individuals over time, while soft biometrics do not uniquely identify individuals and may not persist in the same state over an extended time. We develop methods that enable recognition using 2D ear images. This recognition is performed using a dataset which contains various lighting and pose conditions, as well as time lapse. We explore the growing field of ensemble biometrics, which subdivide a biometric feature into parts, and combine the results of several parts to yield recognition results. We vary the number of parts, the size of each part, and the method used to build each ensemble and report recognition improvements. We also allow the parts to change shape both before and during training, which further improves performance. We perform recognition using soft biometric features extracted from video. Although these features are not as reliable as traditional biometric features, they can still contribute to the recognition process. We find that using clothing color and height yield modest performance results that can be extended on their own or applied to other biometric systems.

6 citations

Journal ArticleDOI
TL;DR: This study investigates the accuracy and verification performance of a series of interpolation methods for recreating a signature image from the time-series data contained in two ISO/IEC data storage formats, and indicates possible best practice in terms of image recreation method, recreated image resolution and temporal sample rate.
Abstract: Human signatures are widely used for biometric authentication. For automatic online signature verification, rather than storing an image of the completed signature, data are represented in the form of a time series of pen position and status information allowing the extraction of temporal-based features. For visualisation purposes, signature images need to be recreated from time-series data. In this study, the authors investigate the accuracy and verification performance of a series of interpolation methods for recreating a signature image from the time-series data contained in two ISO/IEC data storage formats. The authors experiments investigate dynamic data stored at various sample rates and signature images recreated at differing resolutions. Their study indicates possible best practice in terms of image recreation method, recreated image resolution and temporal sample rate and assesses the effect on the accuracy of reconstructed signature data.

6 citations

01 Jan 2011
TL;DR: The results from the neural models trained by Levenberg- Marquardt algorithm is found to provide accuracy in recognition better than the methods presented in the literature.
Abstract: A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. An approach for accurate Biometric Recognition and identification of Human Iris Patterns using Neural Network has been illustrated in (10). The same authors tried by reducing the size of the templates from 20 X 480 to 10 X 480 and concluded that this resulted in saving of computation effort with no loss in accuracy. In this paper, based on the accurate methodology (10(, we extend the work for optimization for Iris Patterns recognition using various neural training model algorithms. The results from the neural models trained by Levenberg- Marquardt algorithm is found to provide accuracy in recognition better than the methods presented in the literature.

6 citations


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