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Relevant bibliographies by topics / Genuine signature

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Academic literature on the topic 'Genuine signature'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Genuine signature"

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Et. al., Kritika Vohra,. "Signature Verification Using Support Vector Machine and Convolution Neural Network." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (2021): 80–89. http://dx.doi.org/10.17762/turcomat.v12i1s.1564.

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Signature is used for recognition of an individual. Signature is considered as a mark that an individual write on a paper for his/her identity or proof. It is used as a unique feature for identifying an individual. It is highly used in social and business functions which gives rise to verification of signature. There are chances of signature getting forged. Hence, the need to identify signature as genuine of forged is utmost important. In this paper, identification of signature as genuine or forged is done using two approaches. First approach is using SVM and second is using CNN. For SVM, pre-

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NELSON, WINSTON, WILLIAM TURIN, and TREVOR HASTIE. "STATISTICAL METHODS FOR ON-LINE SIGNATURE VERIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 03 (1994): 749–70. http://dx.doi.org/10.1142/s0218001494000395.

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Three methods for on-line signature verification are discussed in this paper. They are based on statistical models of features that summarize different aspects of signature shape and the dynamics of signature production. Two of the methods are based on the feature statistics of genuine signatures only. Of these two methods, the simpler one using a Euclidean distance error metric was found to have superior performance when tested on a database of 919 genuine signatures and 330 forgeries. Using a procedure for selecting the individual best 10 out of 22 features, the Euclidean distance method cor

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Starovoitov, V. V., and U. Akhundjanov. "Distribution of local curvature values as a structural feature for off-line handwritten signature verification." «System analysis and applied information science», no. 2 (October 4, 2023): 49–58. http://dx.doi.org/10.21122/2309-4923-2023-2-49-58.

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In the paper, a new feature for describing a digital image of a handwritten signature based on the frequency distribution of the values of the local curvature of the signature contours, is proposed. The calculation of this feature on the binary image of a signature is described in detail. A normalized histogram of distributions of local curvature values for 40 bins is formed. The frequency values recorded as a 40-dimensional vector are called the local curvature code of the signature.During verification, the proximity of signature pairs is determined by correlation between curvature codes and

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Mandal, Ranju, Srikanta Pal, Partha Pratim Roy, Umapada Pal, and Michael Blumenstein. "Spatial Pyramid Matching-based Multi-script Off-line Signature Identification." Journal of the American Society of Questioned Document Examiners 18, no. 1 (2015): 69–75. https://doi.org/10.69525/jasqde.216.

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Among all of the biometric authentication systems, handwritten signatures are considered as the most legally and socially accepted attributes for personal identification. The objective of this investigation is to present an empirical contribution towards the understanding of a signature identification technique involving multi-script off-line signatures. In our experiment, SIFT (Scale-Invariant Feature Transform) descriptors with Spatial Pyramid Matching (SPM)-based approaches have been used for feature extraction of signatures written in multiple scripts. Support Vector Machines (SVMs) are em

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Sangdiah, Nana Suarna, Irfan Ali, and Dendy Indriya Efendi. "Authenticity Accuracy Improvement Through the Analysis of Signature Ownership Using Convolutional Neural Network Algorithm." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 2 (2025): 1289–93. https://doi.org/10.59934/jaiea.v4i2.900.

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This research aims to improve the accuracy of signature authenticity classification using a Convolutional Neural Network (CNN) model, implemented in a web-based application using the Flask framework. In the digital era, signature authentication has become a crucial component in maintaining data security and transaction validity. However, the classification of genuine and forged signatures presents its own challenges due to the unique variations in patterns and styles of each individual. Using a public dataset from Kaggle consisting of 1,084 signature images (620 forged and 464 genuine), the CN

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Diksha Singh, Dileep Kumar, and Poonam Prakash. "Analysis of Signature Patterns: Consistency and Distinctiveness in Handwritten Signatures for Forensic Authentication." Indian Journal of Forensic Medicine & Toxicology 18, no. 1 (2024): 26–34. http://dx.doi.org/10.37506/c15rg139.

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Handwritten signatures are unique behavioural attributes that serve as a distinct form of identification forindividuals. This study focuses on the analysis of signature patterns using various parameters, including aspect ratio, angle of the first letter with respect to the baseline, and the ratio of the area of a circle to its radius within the signature. A dataset consisting of 1200 genuine and 1200 simulated (English) signature samples from 12 individuals were examined using an image analysis tool to record measurements for these parameters. The primary objectives were to investigate the con

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Starovoitov, V. V. "Verification of the person’s dynamic signature on a limited number of samples." Informatics 21, no. 2 (2024): 94–106. http://dx.doi.org/10.37661/1816-0301-2024-21-2-94-106.

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Objectives. The goal of the research is to develop a new person-dependent method for verification of a signature of one person made on a tablet with a stylus in the presence of a limited number of signature samples of this person.Methods. The paper shows how to construct an individual pattern of the dynamic signatures of any person, which is described by points in a multidimensional feature space and is intended for subsequent verification of the authenticity of the signatures of a given person. It is constructed using 5<N<20 samples of genuine human signatures. The pattern forms a conve

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Nicolaides, Kathleen Annunziata. "Using Acceleration/Deceleration Plots in the Forensic Analysis of Electronically Captured Signatures." Journal of the American Society of Questioned Document Examiners 15, no. 2 (2012): 29–43. https://doi.org/10.69525/jasqde.191.

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A research study was conducted to determine if analysis and comparison of acceleration/deceleration plots of signature data captured by electronic signature tablets would provide meaningful evidence in an examination of electronically captured signatures. This research focused on data collected by signature tablets produced by Topaz Systems, Inc., one of the largest suppliers of digital capture devices. William Flynn, in his article “Conducting a Forensic Examination of Electronically Captured Signatures” (published in the June 2012 edition of the ASQDE Journal), noted the visual differences i

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Prajapati, Prakash Ratna, Samiksha Poudel, Madan Baduwal, Subritt Burlakoti, and Sanjeeb Prasad Panday. "Signature Verification using Convolutional Neural Network and Autoencoder." Journal of the Institute of Engineering 16, no. 1 (2021): 33–40. http://dx.doi.org/10.3126/jie.v16i1.36533.

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Signature has been one of the widely used verification biometrics out there. Handwritten signatures are used in cheques, forms, letters, applications, minutes, etc. The Signature of every individual is unique in nature, that is why it is essential that a person’s handwritten signature be uniquely identified. Signature Verification is a widely used method for authenticating any individual during absence. Human verification is prone to inaccuracy and sometimes indecisiveness. This paper presents an investigation of using Convolutional Neural Network (CNN) for Writer-Dependent models in signature

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Starovoitov, V. V. "Verification of normalized online signatures without calculating dynamic features." Informatics 21, no. 4 (2024): 72–84. https://doi.org/10.37661/1816-0301-2024-21-4-72-84.

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Objectives. Study of the method of verification of the authenticity of a human signature made on a tablet with a stylus and given three parameters: coordinates X, Y and pressure on the tablet P.Methods. N genuine dynamic human signatures are given. Data describing different signatures made by one person always have a different number of points. The main variants of normalization of the original signature data are investigated. A model of an individual image of human signatures is built without calculating dynamic features. The method of dynamic time transformation (DTW) is used to compare simi

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Books on the topic "Genuine signature"

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D, Boyd Robert. A rare gift--genuine signatures. Harlo, 1989.

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New Brunswick Burns Monument Committee., ed. Biographical sketch of Robert Burns: With a review of his poems and songs and a genuine portrait of Burns, with his signature, an illustration of the Burns Monument at Fredericton, N.B. and a description of it; also a list of officers and members of the Fredericton Society of St. Andrew since its organization (in 1825) until 1906 and of the New Brunswick Burns Monument Committe, &c. H.A. Cropley, 1996.

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Hagan, William E. A Treatise on Disputed Handwriting and the Determination of Genuine from Forged Signatures. the Character and Composition of Inks, and Their ... Appearance of Written Instruments and Docu. Franklin Classics Trade Press, 2018.

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William E. (William Elijah) Hagan. Treatise on Disputed Handwriting and the Determination of Genuine from Forged Signatures. the Character and Composition of Inks, and Their Determination by Chemical Tests. the Effect of Age As Manifested in the Appearance of Written Instruments And... Creative Media Partners, LLC, 2021.

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Treatise on Disputed Handwriting and the Determination of Genuine from Forged Signatures. the Character and Composition of Inks, and Their Determination by Chemical Tests. the Effect of Age As Manifested in the Appearance of Written Instruments and Docu. Creative Media Partners, LLC, 2023.

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Hagan, William E. Treatise on Disputed Handwriting and the Determination of Genuine from Forged Signatures. the Character and Composition of Inks, and Their Determination by Chemical Tests. the Effect of Age As Manifested in the Appearance of Written Instruments and Docu. Creative Media Partners, LLC, 2018.

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Book chapters on the topic "Genuine signature"

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Jagtap, Amruta B., Dattatray D. Sawat, Ravindra S. Hegadi, and Rajendra S. Hegadi. "Siamese Network for Learning Genuine and Forged Offline Signature Verification." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9187-3_12.

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Madasu, Vamsi Krishna, and Brian C. Lovell. "An Automatic Off-Line Signature Verification and Forgery Detection System." In Pattern Recognition Technologies and Applications. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-807-9.ch004.

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This chapter presents an off-line signature verification and forgery detection system based on fuzzy modeling. The various handwritten signature characteristics and features are first studied and encapsulated to devise a robust verification system. The verification of genuine signatures and detection of forgeries is achieved via angle features extracted using a grid method. The derived features are fuzzified by an exponential membership function, which is modified to include two structural parameters. The structural parameters are devised to take account of possible variations due to handwriti

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Nabi, Ishrat, Akib Mohi Ud Din Khanday, Ishrat Rashid, Fayaz Ahmed Khan, and Rumaan Bashir. "A Comparative Analysis of Signature Recognition Methods." In Recent Advancements in Multimedia Data Processing and Security. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-7216-3.ch007.

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Signature recognition is the process of automatically identifying or verifying an individual's signature to determine its authenticity. The basic motivation of developing signature recognition systems is to check whether a signature has been done by an authorized user /genuine user or an unauthorized user/a forger. The objective of this chapter is to study different algorithms that are used to authenticate and authorize the signatures of the individual. For personal identification and verification, Signatures are the most acceptable and economical way that is used for this purpose. Signature v

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Siegel, Patricia. "Signature Identification: From Pen Stroke to Brush Stroke." In The Expert versus the object. Oxford University PressNew York, NY, 2004. http://dx.doi.org/10.1093/oso/9780195147353.003.0009.

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Abstract The arts of writing, drawing, painting — and the act of signing one’s name — all express the individuality of the artist. The writer’s movement creates forms within the spatial structure of a signature that reflect the writer’s habits and propensities. Each signature deviates from the one before, but an examination of multiple signatures reveals patterns within the natural variation of the writer that are uniquely his or her own. Whether by pen stroke or brush stroke, it is this individuality of repeated movements and overlapping motifs which provides the foundation for comparison. It

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Anderson, Neal G., and Gualtiero Piccinini. "Conclusion." In The Physical Signature of Computation. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/9780191872075.003.0011.

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Abstract This Conclusion summarizes the arguments put forward in this book, places them in the broader context of accounts of physical computation and debates about pancomputationalism, and expands upon them. First, it recaps (i) how we classify computational descriptions of physical systems by their descriptive strength, (ii) what it means to accept descriptions of a given strength as adequate, and (iii) how this leads to our robust mapping account of implementation. Second, it reiterates how the robust mapping account rules out pancomputationalism while accommodating physical computing subst

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Luna, Francisco, Julio César Martínez Romo, Miguel Mora-González, Evelia Martínez-Cano, and Valentín López Rivas. "Handwritten Signature Verification Using Multi Objective Optimization with Genetic Algorithms in a Forensic Architecture." In Logistics Management and Optimization through Hybrid Artificial Intelligence Systems. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0297-7.ch006.

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This chapter presents the use of multi-objective optimization for on-line automatic verification of handwritten signatures; as discriminating features of each signer are used here some functions of time and space of the position of the pen on the paper; these functions are directly used in a multi-objective optimization task in order to obtain high values of false positives indicators (FAR False Acceptance Rate) and false negatives (FFR, false rejection rate). The genetic algorithms are used to create a signer´s model that optimally characterizes him, thus rejecting the skilled forgeries and r

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Yahya Ebrahim, Anwar, and Hoshang Kolivand. "New Attributes Extraction System for Arabic Autograph as Genuine and Forged through a Classification Techniques." In Pattern Recognition [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96561.

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The authentication of writers, handwritten autograph is widely realized throughout the world, the thorough check of the autograph is important before going to the outcome about the signer. The Arabic autograph has unique characteristics; it includes lines, and overlapping. It will be more difficult to realize higher achievement accuracy. This project attention the above difficulty by achieved selected best characteristics of Arabic autograph authentication, characterized by the number of attributes representing for each autograph. Where the objective is to differentiate if an obtain autograph

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de Rond, Mark. "Hawkeye." In Doctors at War. Cornell University Press, 2017. http://dx.doi.org/10.7591/cornell/9781501705489.003.0001.

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In this chapter, the author talks about a general surgeon he calls Hawkeye, whom he met during a weeklong surgical training course at the Royal College of Surgeons in London. Pre-deployment training involved five days practicing on human cadavers made up to resemble recipients of the war's signature wounds. The author describes Hawkeye as someone who has genuine concern for those put in his care, particularly the Royal Marines. He also discusses the three of four required pre-deployment courses: MOST, targeted specifically at surgeons and anesthetists, a Hospital Exercise (Hospex) staged in a

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Mohammed, Linton A., and Lloyd Cunningham. "Examination of genuine, disguised, and simulated signatures." In Forensic Examination of Signatures. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-813029-2.00005-0.

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Kaviya, K., T. Sangeetha, and Avinash P. "PERFORMANCE ANALYSIS OF BIGDATA SECURITY IN BLOCKCHAIN TECHNOLOGY." In Artificial Intelligence and Machine Learning in Smart Wearables. Iterative International Publishers (IIP), Selfypage Developers Pvt Ltd., 2025. https://doi.org/10.58532/nbennuraimlsw1.

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In today's digital age, it is common for parties to exchange sensitive information. Banking information, insurance data, and health records are just a few instances of sensitive data or documents that necessitate a digital interchange. In many circumstances, the transaction takes place between unidentified and untrustworthy persons. As a result, executing the data exchange using a fair nonrepudiation protocol is critical. Nonrepudiation is indisputable proof of one's accountability for the veracity of whatever data he shares/receives in digital communication. This is usually accomplished via t

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Conference papers on the topic "Genuine signature"

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Alsuhimat, Fadi Mohammad, and Fatma Susilawati Mohamad. "Convolutional Neural Network for Offline Signature Verification via Multiple Classifiers." In 7th International Conference on Natural Language Computing (NATL 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112008.

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The signature process is one of the most significant processes used by organizations to preserve the security of information and protect it from unwanted penetration or access. As organizations and individuals move into the digital environment, there is an essential need for a computerized system able to distinguish between genuine and forged signatures in order to protect people's authorization and decide what permissions they have. In this paper, we used Pre-Trained CNN for extracts features from genuine and forged signatures, and three widely used classification algorithms, SVM (Support Vec

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Patil, Swati M., and Poonam V. Gaikwad. "On-Line Signature Verification." In National Conference on Relevance of Engineering and Science for Environment and Society. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.118.46.

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In this paper, we are going to propose a new technique for On-Line signature based on global n local features. In general, shape of an on-line signature is used as a single discriminating feature. Sometimes shape of signature is used alone for verification purposes and sometimes it is used in combination with some other dynamic features such as velocity, pressure and entropy. In proposed system shape of signature is examined using Edge-Detection Algorithm (EDA), pressure points are calculated using Pressure Points Allocation using Clustering (PPAC). So the overall process can be thought as the

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Adamski, Marcin, and Khalid Saeed. "Signature verification by only single genuine sample in offline and online systems." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2015 (ICNAAM 2015). Author(s), 2016. http://dx.doi.org/10.1063/1.4951958.

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Ferrer, Miguel A., Moises Diaz, Cristina Carmona-Duarte, and Rejean Plamondon. "Generating Off-line and On-line Forgeries from On-line Genuine Signatures." In 2019 International Carnahan Conference on Security Technology (ICCST). IEEE, 2019. http://dx.doi.org/10.1109/ccst.2019.8888418.

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Chaudhuri, Chitrita, Atal Chaudhuri, and Aparajita Khan. "Authentication of Offline Signatures Based on Central Tendency of Features and Dynamic Time Warping Values Preserved for Genuine Cases." In 2014 Fourth International Conference of Emerging Applications of Information Technology (EAIT). IEEE, 2014. http://dx.doi.org/10.1109/eait.2014.38.

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Rajagopalan, Vinayaka N., and John M. Vance. "Diagnosing Coupled Lateral-Torsional Vibrations in Turbomachinery." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-50125.

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Rotordynamic instability, commonly observed as subsynchronous vibration, is a serious problem that can cause heavy damage to a turbomachine or make it incapable of operation due to high vibration levels. However, all subsynchronous vibrations are not necessarily unstable. A way to quickly diagnose them would be helpful. In an earlier paper, the authors presented data from experiments that simulated various causes of sub-synchronous vibrations, some causes being genuine rotordynamic instabilities and some others being benign (stable), and identified ways to diagnose and classify the subsynchron

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Rajagopalan, Vinayaka N., and John M. Vance. "Diagnosing Subsynchronous Vibrations: Unstable or Benign." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35694.

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Rotordynamic instability, commonly observed as subsynchronous vibration, is a serious problem that can cause heavy damage to a turbomachine or make it incapable of operation due to high vibration levels. However, all subsynchronous vibrations are not necessarily unstable. If the amplitude of the subsynchronous vibration is large, it can cause damage to seals, bearings, or process wheels. If it is small, the question arises as to whether it has the potential to grow larger (“instability”) or whether it is benign and harmless. A way to know would be helpful. The objective of this study is to sig

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