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Zois EN, Said S, Tsourounis D, Alexandridis A. Subscripto multiplex: a Riemannian symmetric positive definite strategy for offline signature verification. Pattern Recognit Lett 2023. [DOI: 10.1016/j.patrec.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Jiang J, Lai S, Jin L, Zhu Y, Zhang J, Chen B. Forgery-free Signature Verification with Stroke-aware Cycle-consistent Generative Adversarial Network. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.08.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Avola D, Bigdello MJ, Cinque L, Fagioli A, Marini MR. R-SigNet: Reduced space writer-independent feature learning for offline writer-dependent signature verification. Pattern Recognit Lett 2021. [DOI: 10.1016/j.patrec.2021.06.033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Wei Z, Yang S, Xie Y, Li F, Zhao B. SVSV: Online handwritten signature verification based on sound and vibration. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.04.099] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Signature verification using geometrical features and artificial neural network classifier. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05473-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Jain A, Singh SK, Pratap Singh K. Multi‐task learning using GNet features and SVM classifier for signature identification. IET BIOMETRICS 2021. [DOI: 10.1049/bme2.12007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Anamika Jain
- Department of Information Technology Indian Institute of Information Technology Allahabad Uttar Pradesh India
| | - Satish Kumar Singh
- Department of Information Technology Indian Institute of Information Technology Allahabad Uttar Pradesh India
| | - Krishna Pratap Singh
- Department of Information Technology Indian Institute of Information Technology Allahabad Uttar Pradesh India
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An Offline Signature Verification and Forgery Detection Method Based on a Single Known Sample and an Explainable Deep Learning Approach. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10113716] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Signature verification is one of the biometric techniques frequently used for personal identification. In many commercial scenarios, such as bank check payment, the signature verification process is based on human examination of a single known sample. Although there is extensive research on automatic signature verification, yet few attempts have been made to perform the verification based on a single reference sample. In this paper, we propose an off-line handwritten signature verification method based on an explainable deep learning method (deep convolutional neural network, DCNN) and unique local feature extraction approach. We use the open-source dataset, Document Analysis and Recognition (ICDAR) 2011 SigComp, to train our system and verify a questioned signature as genuine or a forgery. All samples used in our testing process are collected from a new author whose signatures are not present in the training or other stages. From the experimental results, we get the accuracy between 94.37% and 99.96%, false rejection rate (FRR) between 5.88% and 0%, false acceptance rate (FAR) between 0.22% and 5.34% in our testing dataset.
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Patch-based offline signature verification using one-class hierarchical deep learning. INT J DOC ANAL RECOG 2019. [DOI: 10.1007/s10032-019-00331-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Maergner P, Pondenkandath V, Alberti M, Liwicki M, Riesen K, Ingold R, Fischer A. Combining graph edit distance and triplet networks for offline signature verification. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2019.06.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Abstract
One of the biometric methods in authentication systems is the writer verification/identification using password handwriting. The main objective of this paper is to present a robust writer verification system by using cursive texts as well as block letter words. To evaluate the system, two datasets have been used. One of them is called Secure Password DB 150, which is composed of 150 users with 18 samples of single character words per user. Another dataset is public and called IAM online handwriting database, and it is composed of 220 users of cursive text samples. Each sample has been defined by a set of features, composed of 67 geometrical, statistical, and temporal features. In order to get more discriminative information, two feature reduction methods have been applied, Fisher Score and Info Gain Attribute Evaluation. Finally, the classification system has been implemented by hold-out cross validation and k-folds cross validation strategies for three different classifiers, K-NN, Naïve Bayes and Bayes Net classifiers. Besides, it has been applied for verification and identification approaches. The best results of 95.38% correct classification are achieved by using the k-nearest neighbor classifier for single character DB. A feature reduction by Info Gain Attribute Evaluation improves the results for Naïve Bayes Classifier to 98.34% for IAM online handwriting DB. It is concluded that the set of features and its reduction are a strong selection for the based-password handwritten writer identification in comparison with the state-of-the-art.
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Zois EN, Tsourounis D, Theodorakopoulos I, Kesidis AL, Economou G. A Comprehensive Study of Sparse Representation Techniques for Offline Signature Verification. ACTA ACUST UNITED AC 2019. [DOI: 10.1109/tbiom.2019.2897802] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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