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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: Novel autonomous signature classification approach based on clustering features is presented, fused with fuzzy membership equation of fuzzy c-means algorithm, based on the features of the signature to verify signature using autonomous self-organized Neural Network Model.
Abstract: This paper proposes an approach to verify signature using autonomous self-organized Neural Network Model , fused with fuzzy membership equation of fuzzy c-means algorithm, based on the features of the signature. To overcome limitations of the functional approach and Parametric approach among the conventional on-line signature recognition approaches, this Paper presents novel autonomous signature classification approach based on clustering features. Thirty-six globa1 features and twelve local features were defined, so that a signature verifying system with FE-SONN that learns them was implemented. It was experimented for total 713 signatures that are composed of 155 original signatures and 180 forged signatures yet 378 original signatures written by oneself. The success rate of this test is more than 97.67 But, a few forged signatures that could not be detected by human eyes could not be done by the system either.

2 citations

Proceedings ArticleDOI
25 Jul 2009
TL;DR: The signature verification using SVD technique is a dynamic method in which when users sign, the system recognizes the signature by analyzing its characters such as acceleration, pressure, and orientation.
Abstract: Online signature verification rests on hypothesis of which for any writer, there will be similarity among signature samples, with some scale variability and small distortion. The signature verification using SVD technique is a dynamic method in which when users sign, the system recognizes the signature by analyzing its characters such as acceleration, pressure, and orientation. The SVD technique is used to find r-singular vectors sensing the maximal energy of the signature data matrix A. The data matrix is obtained using a virtual reality glove, 5DT Data Glove Ultra 14. The r-singular vector is called principle subspace which account for most of the variation in the original data. Having modeled the signature through its r-th principal subspace, the authenticity of the tried signature can be determined by calculating the average distance between its principal subspace and the template signature. The signature verification technique was tested with large number of authentic and forgery signatures and has demonstrated the good potential of this technique.

2 citations

Journal ArticleDOI
01 Mar 2015
TL;DR: This paper proposes Feature-Strengthened Gesture Recognition(FsGr) Model, which can improve the recognition success rate when DTW is used, and presents the performance result of FsGr model, by experimenting the recognition of lower case alphabets.
Abstract: As smart devices get popular, research on gesture recognition using their embedded-accelerometer draw attention. As Dynamic Time Warping(DTW), recently, has been used to perform gesture recognition on data sequence from accelerometer, in this paper we propose Feature-Strengthened Gesture Recognition(FsGr) Model which can improve the recognition success rate when DTW is used. FsGr model defines feature-strengthened parts of data sequences to similar gestures which might produce unsuccessful recognition, and performs additional DTW on them to improve the recognition rate. In training phase, FsGr model identifies sets of similar gestures, and analyze features of gestures per each set. During recognition phase, it makes additional recognition attempt based on the result of feature analysis to improve the recognition success rate, when the result of first recognition attempt belongs to a set of similar gestures. We present the performance result of FsGr model, by experimenting the recognition of lower case alphabets.Keywords:Gesture Recognition, Dynamic Time Warping(DTW), Machine Learning

2 citations

Book ChapterDOI
Jin-Whan Kim1
08 Dec 2011
TL;DR: An algorithm for dynamic signature verification using the latest Smart-phones such as iPhone, android phone and MS windows phone to determine the authentication of signatures by comparing and analyzing various dynamic data such as shape of the signature, writing speed, slant of shape, and the order and number of strokes for personal signatures for the smart-phones.
Abstract: We propose an algorithm for dynamic signature verification using the latest Smart-phones such as iPhone, android phone and MS windows phone. Also, we suggest simple signature patterns, the performance of a dynamic signature verification system, which determine the authentication of signatures by comparing and analyzing various dynamic data such as shape of the signature, writing speed, slant of shape, and the order and number of strokes for personal signatures for the smart-phones. In ubiquitous society, the smart-phone will be very important portable device for the mankind.

2 citations

Journal Article
TL;DR: The basic principles and some key technologies of the whole hand biometric technology are described, and the strengths and weaknesses of a variety of popular single-mode and multi-modal biometric feature are described.
Abstract: The multi-modal biometric recognition of the whole hand is an identification technology using comprehensive feature of fingerprint,hand shape and palm,which have more comprehensive and reliable information and can effectively improve the accuracy of biometric recognition.Firstly,in this paper,the basic principles and some key technologies of the whole hand biometric technology are described.Then,the strengths and weaknesses of a variety of popular single-mode and multi-modal biometric feature are anslysized.Finally,problems and future research directions biometric identification technology are discussed.

2 citations


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