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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: A technique that allows one to identify the faulty condition (open or short circuit) at a termination of a multiconductor transmission line structure by measuring the induced voltage at the other end by using the wavelet theory.
Abstract: This paper describes a technique that allows one to identify the faulty condition (open or short circuit) at a termination of a multiconductor transmission line structure by measuring the induced voltage at the other end. The wavelet theory is used to filter out from the signal the components due to unwanted sources, and to decompose it to obtain the fault's signature. The comparison (or matching) algorithm is substituted by an artificial neural network. Two differently designed neural networks are used to validate the results and the overall procedure is also tested on an experimental set-up.

17 citations

Proceedings ArticleDOI
01 Dec 2011
TL;DR: This paper presents a review of some online/dynamic and offline/static signature verification system that have been proposed from year 2000 to 2010 to make a novice summary and conclusion for them.
Abstract: This paper presents a review of some online/dynamic and offline/static signature verification system that have been proposed from year 2000 to 2010. There are numerous signature verification systems algorithms and methods been proposed in the last decade. This paper will mainly focus on discussing the signature verification techniques from year 2000 onwards to make a novice summary and conclusion for them.

17 citations

Proceedings ArticleDOI
01 Aug 2017
TL;DR: Comparisons and evaluations of recognition accuracy and running speed show that PCA + SVM achieves the best recognition result, which is over 95% for certain training data and eigenface sizes.
Abstract: Facial recognition is a challenging problem in image processing and machine learning areas. Since widespread applications of facial recognition make it a valuable research topic, this work tries to develop some new facial recognition systems that have both high recognition accuracy and fast running speed. Efforts are made to design facial recognition systems by combining different algorithms. Comparisons and evaluations of recognition accuracy and running speed show that PCA + SVM achieves the best recognition result, which is over 95% for certain training data and eigenface sizes. Also, PCA + KNN achieves the balance between recognition accuracy and running speed.

17 citations

Proceedings ArticleDOI
03 Mar 2015
TL;DR: The design, acquisition process and a baseline evaluation of e-BioSign, a new database of dynamic signature and handwriting, and the use of finger for signing achieves good results for the case of random forgeries, but the performance is degraded significantly for the cases of skilled forgeries compared to the case using the pen stylus.
Abstract: This paper describes the design, acquisition process and a baseline evaluation of e-BioSign, a new database of dynamic signature and handwriting. e-BioSign is comprised of 5 devices in total, threeWacom devices (DTU-500, DTU-530 and STU 1031) specifically designed to capture dynamic signatures and handwriting, and two Samsung general purpose tablets (Samsung Galaxy Note 10.1 and Samsung ATIV). For these two Samsung tablets data is collected using a pen stylus but also the finger to study the performance of signature verification in a mobile scenario. Data was collected in two sessions for 70 subjects, and includes dynamic information of the signature, the full name and number sequences. For signature and the full name skilled forgeries were also performed. A signature baseline evaluation is carried out for a predefined recognition system based on DTW, achieving a benchmark performance for each of the devices. The use of finger for signing achieves good results for the case of random forgeries (less than 1% EER), but the performance is degraded significantly for the case of skilled forgeries compared to the case using the pen stylus.

17 citations


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