Open AccessJournal Article
Handwritten signature identification using basic concepts of graph theory
TLDR
Previous work in the field of signature and writer identification is presented to show the historical development of the idea and a new promising approach in handwritten signature identification based on some basic concepts of graph theory is defined.Abstract:
Handwritten signature is being used in various applications on daily basis. The problem arises when someone decides to imitate our signature and steal our identity. Therefore, there is a need for adequate protection of signatures and a need for systems that can, with a great degree of certainty, identify who is the signatory. This paper presents previous work in the field of signature and writer identification to show the historical development of the idea and defines a new promising approach in handwritten signature identification based on some basic concepts of graph theory. This principle can be implemented on both on-line handwritten signature recognition systems and off-line handwritten signature recognition systems. Using graph norm for fast classification (filtration of potential users), followed by comparison of each signature graph concepts value against values stored in database, the system reports 94.25% identification accuracy.read more
Citations
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Offline handwritten signature recognition using adaptive variance reduction
TL;DR: Experimental results show that the adaptive variance reduction procedure helps improve the recognition rate when compared to the traditional schemes without adaptive variance Reduction, including histogram of gradient (HOG) and pyramid histogramof gradient (PHOG) techniques.
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Offline Signature Recognition Using Centroids of Local Binary Vectors
Nada Najeel Kamal,Loay E. George +1 more
TL;DR: This work proposes a new method to recognize handwritten signature in an offline manner using the centroid of two local binary vectors, the horizontal vector and the vertical vector.
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Feature Engineering Techniques to Improve Identification Accuracy for Offline Signature Case-Bases
TL;DR: Signature identification accuracy is found promising when compared with other machine learning techniques and a few existing well-known approaches.
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Learning non-Gaussian graphical models via Hessian scores and triangular transport
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A signature identification system with principal component analysis and stentiford thinning algorithms
Olatubosun Olabode,AdeniyiJide Kehinde,Akinyede Olufemi,Oluwadare A. Samuel,Fasoranbaku A. Olusoga +4 more
TL;DR: This paper attempt design and implement an algorithm for handwritten signature identification, which consists of signature acquisition, preprocessing, features extraction and matching stages, and has a FAR of 4% and an FRR of 6% for offline signatures.
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