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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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01 Jan 2011
TL;DR: This work proposes an intelligent network that works on the features like angle feature and energy density of the signature for the verification and also a comparative statement is made between them in order to see which method provides better results.
Abstract: Hand written signature used every day at various places for the authentication of a person, but a signature of a person may not be same at different time or it may be generated by some fraud way. So, a system is required for verification of the signature. The signature verification can be done either online or offline, here we are using offline signature verification network. In the proposed system the signatures is taking as an image and apply image processing technique to make the system effective. Here we propose an intelligent network that works on the features like angle feature and energy density of the signature for the verification and also a comparative statement is made between them in order to see which method provides better results.

5 citations

Dissertation
13 Oct 2014
TL;DR: This thesis compares three different machine learning tools, which include Hidden Markov Models (HMMs), Support Vector Machines (SVMs), and Dynamic Time Warping (DTW), to recognize a number of different gestures classes and shows these results to outperform all other thus far.
Abstract: Classification of human movement is a large field of interest to Human-Machine Interface researchers. The reason for this lies in the large emphasis humans place on gestures while communicating with each other and while interacting with machines. Such gestures can be digitized in a number of ways, including both passive methods, such as cameras, and active methods, such as wearable sensors. While passive methods might be the ideal, they are not always feasible, especially when dealing in unstructured environments. Instead, wearable sensors have gained interest as a method of gesture classification, especially in the upper limbs. Lower arm movements are made up of a combination of multiple electrical signals known as Motor Unit Action Potentials (MUAPs). These signals can be recorded from surface electrodes placed on the surface of the skin, and used for prosthetic control, sign language recognition, human machine interface, and a myriad of other applications. In order to move a step closer to these goal applications, this thesis compares three different machine learning tools, which include Hidden Markov Models (HMMs), Support Vector Machines (SVMs), and Dynamic Time Warping (DTW), to recognize a number of different gestures classes. It further contrasts the applicability of these tools to noisy data in the form of the Ninapro dataset, a benchmarking tool put forth by a conglomerate of universities. Using this dataset as a basis, this work paves a path for the analysis required to optimize each of the three classifiers. Ultimately, care is taken to compare the three classifiers for their utility against noisy data, and a comparison is made against classification results put forth by other researchers in the field. The outcome of this work is 90+ % recognition of individual gestures from the Ninapro dataset whilst using two of the three distinct classifiers. Comparison against previous works by other researchers shows these results to outperform all other thus far. Through further work with these tools, an end user might control a robotic or prosthetic arm, or translate sign language, or perhaps simply interact with a computer.

5 citations

Book ChapterDOI
01 Jan 2006
TL;DR: The experimental results show that the method not only reduces the amount of data to be stored, but also minimizes the duration of the whole authentication processing and increases the efficiency of signature verification.
Abstract: On-line signature verification is one of the most accepted means for personal verification. This paper proposes an on-line signature verification method based on Wavelet Transform (WT). Firstly, the method uses wavelet transform to exact characteristic points of 3-axis force and 2-dimension coordinate of signature obtained by the F-Tablet. And then it builds 5-dimension feature sequences and dynamically creates multi-templates using clustering. Finally, after the fusion of the above-mentioned 5-dimension feature sequences, whether the signature is genuine or not is decided by majority voting scheme. Experimenting on a signature database acquired by F-Tablet, the performance evaluation in even EER (Equal Error Rate) was improved to 2.83%. The experimental results show that the method not only reduces the amount of data to be stored, but also minimizes the duration of the whole authentication processing and increases the efficiency of signature verification.

5 citations

Journal ArticleDOI
TL;DR: The aim of this thesis is to solve the face-detection in the first attempt using the Haar-cascade classifier from images containing simple and complex backgrounds by using a modified Haar cascade algorithm.
Abstract: Amid the previous three decades, the topic of image processing has gained vital name and recognition among researchers because of their frequent look in varied and widespread applications within the field of various branches of science and engineering. As an example, image processing is helpful to issues in signature recognition, digital video processing, remote sensing and finance. Image processing models are used for detecting the face. The aim of this thesis is to solve the face-detection in the first attempt using the Haar-cascade classifier from images containing simple and complex backgrounds. It is one of the preeminent detectors in terms of reliability and speed. We introduced a new method to deal with the frontal face images by using a modified Haar cascade algorithm. By using this algorithm, we can detect the image as well as the coordinates. The main attraction of this paper is to solve different types of images having one object, two objects, and three objects which can’t be solved by any of the existing methods but can be solved by our proposed method.

5 citations

Patent
05 May 2014
TL;DR: In this paper, a method and device for matching signatures on the basis of motion signature information is presented, where a first signature and at least one second signature that are to be matched are acquired, where the first signature is generated and matched based on the motion signatures corresponding thereto; and, the first signatures and the second signatures are matched on a basis of the motion signature to acquire a corresponding match result.
Abstract: The present invention provides a method and device for matching signatures on the basis of motion signature information. A first signature and at least one second signature that are to be matched are acquired, where the first signature is generated on the basis of the motion signature information corresponding thereto; and, the first signature and the second signature are matched on the basis of the motion signature information to acquire a corresponding match result. The present invention matches the signatures on the basis of the motion signature information corresponding to the signatures that are to be matched, thus increasing the accuracy and efficiency in signature matching, and enhancing user experience for the user.

5 citations


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