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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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2
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Biometrics recognition using deep learning: a survey. Artif Intell Rev 2023. [DOI: 10.1007/s10462-022-10237-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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3
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Lai S, Jin L, Zhu Y, Li Z, Lin L. SynSig2Vec: Forgery-Free Learning of Dynamic Signature Representations by Sigma Lognormal-Based Synthesis and 1D CNN. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:6472-6485. [PMID: 34101587 DOI: 10.1109/tpami.2021.3087619] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Handwritten signature verification is a challenging task because signatures of a writer may be skillfully imitated by a forger. As skilled forgeries are generally difficult to acquire for training, in this paper, we propose a deep learning-based dynamic signature verification framework, SynSig2Vec, to address the skilled forgery attack without training with any skilled forgeries. Specifically, SynSig2Vec consists of a novel learning-by-synthesis method for training and a 1D convolutional neural network model, called Sig2Vec, for signature representation extraction. The learning-by-synthesis method first applies the Sigma Lognormal model to synthesize signatures with different distortion levels for genuine template signatures, and then learns to rank these synthesized samples in a learnable representation space based on average precision optimization. The representation space is achieved by the proposed Sig2Vec model, which is designed to extract fixed-length representations from dynamic signatures of arbitrary lengths. Through this training method, the Sig2Vec model can extract extremely effective signature representations for verification. Our SynSig2Vec framework requires only genuine signatures for training, yet achieves state-of-the-art performance on the largest dynamic signature database to date, DeepSignDB, in both skilled forgery and random forgery scenarios. Source codes of SynSig2Vec will be available at https://github.com/LaiSongxuan/SynSig2Vec.
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Asemi A, Maghooli K, Rahatabad FN, Azadeh H. Handwritten signatures verification based on arm and hand muscles synergy. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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5
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Kumar R, Saraswat M, Ather D, Mumtaz Bhutta MN, Basheer S, Thakur RN. Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4406101. [PMID: 35789609 PMCID: PMC9250446 DOI: 10.1155/2022/4406101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/23/2022] [Accepted: 05/16/2022] [Indexed: 01/15/2023]
Abstract
Signature verification is the widely used biometric verification method for maintaining individual privacy. It is generally used in legal documents and in financial transactions. A vast range of research has been done so far to tackle different system issues, but there are various hot issues that remain unaddressed. The scale and orientation of the signatures are some issues to address, and the deformation of the signature within the genuine examples is the most critical for the verification system. The extent of this deformation is the basis for verifying a given sample as a genuine or forgery signature, but in the case of only a single signature sample for a class, the intra-class variation is not available for decision-making, making the task difficult. Besides this, most real-world signature verification repositories have only one genuine sample, and the verification system is abiding to verify the query signature with a single target sample. In this work, we utilize a two-phase system requiring only one target signature image to verify a query signature image. It takes care of the target signature's scaling, orientation, and spatial translation in the first phase. It creates a transformed signature image utilizing the affine transformation matrix predicted by a deep neural network. The second phase uses this transformed sample image and verifies the given sample as the target signature with the help of another deep neural network. The GPDS synthetic and MCYT datasets are used for the experimental analysis. The performance analysis of the proposed method is carried out on FAR, FRR, and AER measures. The proposed method obtained leading performance with 3.56 average error rate (AER) on GPDS synthetic, 4.15 AER on CEDAR, and 3.51 AER on MCYT-75 datasets.
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Affiliation(s)
- Rakesh Kumar
- Department of Computer Engineering & Applications, GLA University Mathura, Mathura-281406, India
| | - Mala Saraswat
- Department of Computer Science and Engineering, ABES Engineering College Ghaziabad, India
| | - Danish Ather
- Department of Computer Science & Engineering, School of Engineering & Technology Sharda University, Grater Noida, India
| | - Muhammad Nasir Mumtaz Bhutta
- Computer Science and Information Technology (CSIT), College of Engineering, Abu Dhabi University, P.O. Box 5991, Abu Dhabi, UAE
| | - Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
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Jain V, Gupta P, Chaudhry A, Batra M, Hemanth DJ. A Modified Deep Convolution Siamese Network for Writer-Independent Signature Verification. INT J UNCERTAIN FUZZ 2022. [DOI: 10.1142/s0218488522400177] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper problem of offline signature verification has been discussed with a novel high-performance convolution Siamese network. The paper proposes modifications in the already existing convolution Siamese network. The proposed method makes use of the Batch Normalization technique instead of Local Response Normalization to achieve better accuracy. The regularization factor has been added in the fully connected layers of the convolution neural network to deal with the problem of overfitting. Apart from this, a wide range of learning rates are provided during the training of the model and optimal one having the least validation loss is used. To evaluate the proposed changes and compare the results with the existing solution, our model is validated on three benchmarks datasets viz. CEDAR, BHSig260, and GPDS Synthetic Signature Corpus. The evaluation is done via two methods firstly by Test-Train validation and then by K-fold cross-validation (K = 5), to test the skill of our model. We show that the proposed modified Siamese network outperforms all the prior results for offline signature verification. One of the major advantages of our system is its capability of handling an unlimited number of new users which is the drawback of many research works done in the past.
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Affiliation(s)
- Vanita Jain
- Bharati Vidyapeeth’s College of Engineering, New Delhi, India
| | | | | | - Manas Batra
- Tata Consultancy Service Ltd, New Delhi, India
| | - D. Jude Hemanth
- Karunya Institute of Technology and Science, Coimbatore, India
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Jacques L, Silvia B, Raymond M, Franco T. Bayesian evaluation of dynamic signatures in operational conditions. Forensic Sci Int 2022; 332:111173. [PMID: 35066400 DOI: 10.1016/j.forsciint.2022.111173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/04/2021] [Accepted: 01/01/2022] [Indexed: 11/04/2022]
Abstract
Forensic handwriting examiners (FHE) activities are focused on comparative analysis of handwritten objects such as signatures. Their role is to provide and evaluate evidence for and against the authenticity of a questioned signature. In recent years, cases involving handwritten signatures captured on electronic devices have become more commonplace. These so-called 'dynamic signatures' (also known as 'digitally captured signatures') are much different from paper-based signatures. Not only does the medium of recording differ, but also the type, volume of data and features are different from the pattern-based evidence that makes up paper-based signatures. Recent developments in forensic science - including signature examination - have led to the adoption of evaluative probabilistic methodologies in many disciplines [see, e.g. ENFSI 1915 Guidelines]. In the current paper, a probabilistic model to evaluate signature evidence in the form of multivariate data, as proposed and described in Wacom Europe GmbH (2019), is adopted. Topics like data sparsity, joint evaluation of multiple features and feature selection are investigated. Performed experimental studies showed an accuracy rate above 90% even when a limited number (5) of reference signatures was available. The performances of a multivariate approach are compared with those characterizing a so-called multiplicative approach where variables (features) are taken as independent and the Bayes' factor (BF) is obtained as the product of univariate BFs associated to each selected feature. The simplicity of this latter approach is, however, accompanied by severe issues about the reliability of results. The use of a multivariate approach is therefore highly recommended. Finally, the evidential values in correspondence of alternative feature sets are compared. Results suggest that discriminative features are writer-related and necessitate a case-specific selection.
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Affiliation(s)
- Linden Jacques
- School of Criminal Justice, Université de Lausanne, CH-1015 Lausanne-Dorigny, Switzerland.
| | - Bozza Silvia
- School of Criminal Justice, Université de Lausanne, CH-1015 Lausanne-Dorigny, Switzerland; Dipartimento di Economia, Università Ca' Foscari Venezia, Dorsoduro, 3246, 30123 Venezia, VE, Italy
| | - Marquis Raymond
- School of Criminal Justice, Université de Lausanne, CH-1015 Lausanne-Dorigny, Switzerland
| | - Taroni Franco
- School of Criminal Justice, Université de Lausanne, CH-1015 Lausanne-Dorigny, Switzerland
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Online Signature Verification Systems on a Low-Cost FPGA. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app12010378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper describes three different approaches for the implementation of an online signature verification system on a low-cost FPGA. The system is based on an algorithm, which operates on real numbers using the double-precision floating-point IEEE 754 format. The double-precision computations are replaced by simpler formats, without affecting the biometrics performance, in order to permit efficient implementations on low-cost FPGA families. The first approach is an embedded system based on MicroBlaze, a 32-bit soft-core microprocessor designed for Xilinx FPGAs, which can be configured by including a single-precision floating-point unit (FPU). The second implementation attaches a hardware accelerator to the embedded system to reduce the execution time on floating-point vectors. The last approach is a custom computing system, which is built from a large set of arithmetic circuits that replace the floating-point data with a more efficient representation based on fixed-point format. The latter system provides a very high runtime acceleration factor at the expense of using a large number of FPGA resources, a complex development cycle and no flexibility since it cannot be adapted to other biometric algorithms. By contrast, the first system provides just the opposite features, while the second approach is a mixed solution between both of them. The experimental results show that both the hardware accelerator and the custom computing system reduce the execution time by a factor ×7.6 and ×201 but increase the logic FPGA resources by a factor ×2.3 and ×5.2, respectively, in comparison with the MicroBlaze embedded system.
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Khoh WH, Pang YH, Teoh ABJ, Ooi SY. In-air hand gesture signature using transfer learning and its forgery attack. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.108033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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10
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Almehmadi A. A biometric‐based verification system for handwritten image‐based signatures using audio to image matching. IET BIOMETRICS 2021. [DOI: 10.1049/bme2.12059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Abdulaziz Almehmadi
- Department of IT Faculty of Computing and IT SNCS Research Center University of Tabuk Tabuk Saudi Arabia
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11
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Saleem M, Kovari B. Online signature verification using signature down-sampling and signer-dependent sampling frequency. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06536-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
AbstractOnline signature verification considers signatures as time sequences of different measurements of the signing instrument. These signals are captured on digital devices and therefore consist of a discrete number of samples. To enrich or simplify this information, several verifiers employ resampling and interpolation as a preprocessing step to improve their results; however, their design decisions may be difficult to generalize. This study investigates the direct effect of the sampling rate of the input signals on the accuracy of online signature verification systems without using interpolation techniques and proposes a novel online signature verification system based on a signer-dependent sampling frequency. Twenty verifier configurations were created for five different public signature databases and a variety of popular preprocessing approaches and evaluated for 20–40 different sampling rates. Our results show that there is an optimal range for the sampling frequency and the number of sample points that minimizes the error rate of a verifier. A sampling frequency range of 15–50 Hz and a signature point count of 60–240 provided the best accuracies in our experiments. As expected, lower ranges showed inaccurate results; interestingly, however, higher frequencies often decreased the verification accuracy. The results show that one can achieve better or at least the same verification accuracies faster by down-sampling the online signatures before further processing. The proposed system achieved competitive results to state-of-the-art systems for different databases by using the optimal sampling frequency. We also studied the effect of choosing individual sampling frequencies for each signer and proposed a signature verification system based on signer-dependent sampling frequency. The proposed system was tested using 500 different verification methods and improved the accuracy in 92% of the test cases compared to the usage of the original frequency.
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12
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Online Handwriting, Signature and Touch Dynamics: Tasks and Potential Applications in the Field of Security and Health. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09938-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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13
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Screening of Parkinson's Disease Using Geometric Features Extracted from Spiral Drawings. Brain Sci 2021; 11:brainsci11101297. [PMID: 34679363 PMCID: PMC8533717 DOI: 10.3390/brainsci11101297] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 12/23/2022] Open
Abstract
Conventional means of Parkinson’s Disease (PD) screening rely on qualitative tests typically administered by trained neurologists. Tablet technologies that enable data collection during handwriting and drawing tasks may provide low-cost, portable, and instantaneous quantitative methods for high-throughput PD screening. However, past efforts to use data from tablet-based drawing processes to distinguish between PD and control populations have demonstrated only moderate classification ability. Focusing on digitized drawings of Archimedean spirals, the present study utilized data from the open-access ParkinsonHW dataset to improve existing PD drawing diagnostic pipelines. Random forest classifiers were constructed using previously documented features and highly-predictive, newly-proposed features that leverage the many unique mathematical characteristics of the Archimedean spiral. This approach yielded an AUC of 0.999 on the particular dataset we tested on, and more importantly identified interpretable features with good promise for generalization across diverse patient cohorts. It demonstrated the potency of mathematical relationships inherent to the drawing shape and the usefulness of sparse feature sets and simple models, which further enhance interpretability, in the face of limited sample size. The results of this study also inform suggestions for future drawing task design and data analytics (feature extraction, shape selection, task diversity, drawing templates, and data sharing).
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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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15
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Negrini Neto O, Sarkis JES, Siqueira AN, Ribeiro LAS, Freitas AZ. A new method for measuring pen pressure in forensic handwriting analysis - a proof of concept study. Analyst 2021; 146:1973-1980. [PMID: 33502395 DOI: 10.1039/d0an01787f] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
This work describes a new method for the analysis of handwritten documents through a system composed of a pre-selector optical analyser equipped with light sources of different wavelengths coupled with bandpass filters combined with an optical coherence tomography (OCT) instrument. The optical analyser identifies regions with different pen pressures on the paper using specific wavelengths from ultraviolet (UV) to infrared (IR) and bandpass filters. Then the selected regions are analysed with a coherence tomography analyser to measure the depth of grooves and capture three-dimensional images. With this methodology, it is possible to identify similarities, or differences, between the pieces of evidence under investigation, increasing the possibility of correct attribution concerning the authorship of the signature and we also showed that this feature is independent of the paper substrate. In this work, a new strategy will be presented to categorize and quantify pen pressure in order to aid a better response for a forensic examiner. Thereby, from the observed areas that display higher pressures (more significant grooves), it is possible to determine the authorship of the signature.
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Affiliation(s)
- O Negrini Neto
- Center for Lasers and Applications, Nuclear and Energy Research Institute, Av. Prof. Lineu Prestes, 2242, São Paulo, SP CEP05508-000, Brasil.
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17
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Rodríguez WDA, Amaral GJA, Nascimento ADC, Ferreira JA. Information criteria in classification: new divergence-based classifiers. J STAT COMPUT SIM 2020. [DOI: 10.1080/00949655.2020.1798445] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
| | - Getúlio J. A. Amaral
- Departamento de Estatística, Universidade Federal de Pernambuco, Recife, PE, Brasil
| | | | - Jodavid A. Ferreira
- Departamento de Estatística, Universidade Federal de Pernambuco, Recife, PE, Brasil
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18
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Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures. SENSORS 2020; 20:s20030933. [PMID: 32050606 PMCID: PMC7039240 DOI: 10.3390/s20030933] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 02/04/2020] [Accepted: 02/04/2020] [Indexed: 11/17/2022]
Abstract
We aim at enhancing personal identity security on mobile touch-screen sensors by augmenting handwritten signatures with specific additional information at the enrollment phase. Our former works on several available and private data sets acquired on different sensors demonstrated that there are different categories of signatures that emerge automatically with clustering techniques, based on an entropy-based data quality measure. The behavior of such categories is totally different when confronted to automatic verification systems in terms of vulnerability to attacks. In this paper, we propose a novel and original strategy to reinforce identity security by enhancing signature resistance to attacks, assessed per signature category, both in terms of data quality and verification performance. This strategy operates upstream from the verification system, at the sensor level, by enriching the information content of signatures with personal handwritten inputs of different types. We study this strategy on different signature types of 74 users, acquired in uncontrolled mobile conditions on a largely deployed mobile touch-screen sensor. Our analysis per writer category revealed that adding alphanumeric (date) and handwriting (place) information to the usual signature is the most powerful augmented signature type in terms of verification performance. The relative improvement for all user categories is of at least 93% compared to the usual signature.
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19
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Off-line handwritten signature verification using compositional synthetic generation of signatures and Siamese Neural Networks. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.09.041] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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20
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Diaz M, Ferrer MA, Quintana JJ. Anthropomorphic Features for On-Line Signatures. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019; 41:2807-2819. [PMID: 30207948 DOI: 10.1109/tpami.2018.2869163] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Many features have been proposed in on-line signature verification. Generally, these features rely on the position of the on-line signature samples and their dynamic properties, as recorded by a tablet. This paper proposes a novel feature space to describe efficiently on-line signatures. Since producing a signature requires a skeletal arm system and its associated muscles, the new feature space is based on characterizing the movement of the shoulder, the elbow and the wrist joints when signing. As this motion is not directly obtained from a digital tablet, the new features are calculated by means of a virtual skeletal arm (VSA) model, which simulates the architecture of a real arm and forearm. Specifically, the VSA motion is described by its 3D joint position and its joint angles. These anthropomorphic features are worked out from both pen position and orientation through the VSA forward and direct kinematic model. The anthropomorphic features' robustness is proved by achieving state-of-the-art performance with several verifiers and multiple benchmarks on third party signature databases, which were collected with different devices and in different languages and scripts.
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Online Signature Verification Based on a Single Template via Elastic Curve Matching. SENSORS 2019; 19:s19224858. [PMID: 31703448 PMCID: PMC6891754 DOI: 10.3390/s19224858] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 10/27/2019] [Accepted: 10/29/2019] [Indexed: 12/01/2022]
Abstract
Person verification using online handwritten signatures is one of the most widely researched behavior-biometrics. Many signature verification systems typically require five, ten, or even more signatures for an enrolled user to provide an accurate verification of the claimed identity. To mitigate this drawback, this paper proposes a new elastic curve matching using only one reference signature, which we have named the curve similarity model (CSM). In the CSM, we give a new definition of curve similarity and its calculation method. We use evolutionary computation (EC) to search for the optimal matching between two curves under different similarity transformations, so as to obtain the similarity distance between two curves. Referring to the geometric similarity property, curve similarity can realize translation, stretching and rotation transformation between curves, thus adapting to the inconsistency of signature size, position and rotation angle in signature curves. In the matching process of signature curves, we design a sectional optimal matching algorithm. On this basis, for each section, we develop a new consistent and discriminative fusion feature extraction for identifying the similarity of signature curves. The experimental results show that our system achieves the same performance with five samples assessed with multiple state-of-the-art automatic signature verifiers and multiple datasets. Furthermore, it suggests that our system, with a single reference signature, is capable of achieving a similar performance to other systems with up to five signatures trained.
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Tolosana R, Vera‐Rodriguez R, Fierrez J, Ortega‐Garcia J. Reducing the template ageing effect in on‐line signature biometrics. IET BIOMETRICS 2019. [DOI: 10.1049/iet-bmt.2018.5259] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Ruben Tolosana
- BiDA Lab – Biometrics and Data Pattern Analytics LaboratoryUniversidad Autonoma de Madrid28049MadridSpain
| | - Ruben Vera‐Rodriguez
- BiDA Lab – Biometrics and Data Pattern Analytics LaboratoryUniversidad Autonoma de Madrid28049MadridSpain
| | - Julian Fierrez
- BiDA Lab – Biometrics and Data Pattern Analytics LaboratoryUniversidad Autonoma de Madrid28049MadridSpain
| | - Javier Ortega‐Garcia
- BiDA Lab – Biometrics and Data Pattern Analytics LaboratoryUniversidad Autonoma de Madrid28049MadridSpain
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23
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Biopen–Fusing password choice and biometric interaction at presentation level. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2018.04.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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24
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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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25
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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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26
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Shariatmadari S, Emadi S, Akbari Y. Nonlinear Dynamics Tools for Offline Signature Verification Using One-class Gaussian Process. INT J PATTERN RECOGN 2019. [DOI: 10.1142/s0218001420530018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
One of the major problems in biometrics and in document forensics is the offline mode of signature verification. This study aims to present a novel approach of verifying an individual’s signature through offline images of handwriting. The approach proposed here relies on a global method which considers signature images as waveforms. First, image decompositions are in terms of a series of wavelet sub-bands at some specific levels. Wavelet sub-bands are then extended so as to obtain waveforms. Each waveform is quantized by two Nonlinear Dynamics Tools in order to generate feature vectors. Multi-Resolution Box-Counting (MRBC) fractal dimension algorithm as well as probabilistic finite state automata (PFSA) are applied separately to signature waveforms. In the training and verification phase, we propose the one-class Gaussian process (GP) priors based on writer-independent approach. As one of the main parameters, optimal decision threshold is selected from False Accept Rate (FAR) and False Reject Rate (FRR) curves. The presented system was tested on two Persian databases (PHBC and UTSig) as well as on two Latin databases (MCYT-75 and CEDAR). In fact, the results produced by this method were generally better in terms of the four signature databases than the state-of-the-art results.
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Affiliation(s)
- Sima Shariatmadari
- Department of Computer Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran
| | - Sima Emadi
- Department of Computer Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran
| | - Younes Akbari
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
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28
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Jia Y, Huang L, Chen H. A Two-Stage Method for Online Signature Verification Using Shape Contexts and Function Features. SENSORS (BASEL, SWITZERLAND) 2019; 19:E1808. [PMID: 31014033 PMCID: PMC6515562 DOI: 10.3390/s19081808] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 04/11/2019] [Accepted: 04/13/2019] [Indexed: 11/16/2022]
Abstract
As a behavioral biometric trait, an online signature is extensively used to verify a person's identity in many applications. In this paper, we present a method using shape contexts and function features as well as a two-stage strategy for accurate online signature verification. Specifically, in the first stage, features of shape contexts are extracted from the input and classification is made based on distance metric. Only the inputs passing by the first stage are represented by a set of function features and verified. To improve the matching accuracy and efficiency, we propose shape context-dynamic time warping (SC-DTW) to compare the test signature with the enrolled reference ones based on the extracted function features. Then, classification based on interval-valued symbolic representation is employed to decide if the test signature is a genuine one. The proposed method is evaluated on SVC2004 Task 2 achieving an Equal Error Rate of 2.39% which is competitive to the state-of-the-art approaches. The experiment results demonstrate the effectiveness of the proposed method.
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Affiliation(s)
- Yu Jia
- School of Electronic and Information Engineering, Beijing Jiaotong University, No. 3 Shangyuancun Haidian District, Beijing 100044, China.
| | - Linlin Huang
- School of Electronic and Information Engineering, Beijing Jiaotong University, No. 3 Shangyuancun Haidian District, Beijing 100044, China.
| | - Houjin Chen
- School of Electronic and Information Engineering, Beijing Jiaotong University, No. 3 Shangyuancun Haidian District, Beijing 100044, China.
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Melo VK, Bezerra BLD, Impedovo D, Pirlo G, Lundgren A. Deep learning approach to generate offline handwritten signatures based on online samples. IET BIOMETRICS 2019. [DOI: 10.1049/iet-bmt.2018.5091] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
| | | | - Donato Impedovo
- Dipartimento di InformaticaUniversità degli Studi di BariBariItaly
| | - Giuseppe Pirlo
- Dipartimento di InformaticaUniversità degli Studi di BariBariItaly
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31
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Hadjadj I, Gattal A, Djeddi C, Ayad M, Siddiqi I, Abass F. Offline Signature Verification Using Textural Descriptors. PATTERN RECOGNITION AND IMAGE ANALYSIS 2019. [DOI: 10.1007/978-3-030-31321-0_16] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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32
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Impedovo D, Pirlo G. Dynamic Handwriting Analysis for the Assessment of Neurodegenerative Diseases: A Pattern Recognition Perspective. IEEE Rev Biomed Eng 2019; 12:209-220. [DOI: 10.1109/rbme.2018.2840679] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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33
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Usage of autoencoders and Siamese networks for online handwritten signature verification. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3844-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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34
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Okawa M. From BoVW to VLAD with KAZE features: Offline signature verification considering cognitive processes of forensic experts. Pattern Recognit Lett 2018. [DOI: 10.1016/j.patrec.2018.05.019] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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35
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Dynamic signatures: A review of dynamic feature variation and forensic methodology. Forensic Sci Int 2018; 291:216-229. [DOI: 10.1016/j.forsciint.2018.08.021] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 08/20/2018] [Indexed: 11/19/2022]
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36
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37
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Fixed-sized representation learning from offline handwritten signatures of different sizes. INT J DOC ANAL RECOG 2018. [DOI: 10.1007/s10032-018-0301-6] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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38
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Zeinali H, BabaAli B, Hadian H. Online signature verification using i‐vector representation. IET BIOMETRICS 2018. [DOI: 10.1049/iet-bmt.2017.0059] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Hossein Zeinali
- Department of Computer EngineeringSharif University of TechnologyTehranIran
| | - Bagher BabaAli
- School of Mathematics, Statistics and Computer ScienceUniversity of TehranTehranIran
| | - Hossein Hadian
- Department of Computer EngineeringSharif University of TechnologyTehranIran
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Sharma A, Sundaram S. On the Exploration of Information From the DTW Cost Matrix for Online Signature Verification. IEEE TRANSACTIONS ON CYBERNETICS 2018; 48:611-624. [PMID: 28103568 DOI: 10.1109/tcyb.2017.2647826] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
This paper explores the utility of information derived from the dynamic time warping (DTW) cost matrix for the problem of online signature verification. The prior works in literature primarily utilize only the DTW scores to authenticate a test signature. To the best of our knowledge, the characteristics of the warping path (used for the alignment) in the cost matrix is hardly exploited for verification of online signatures. Accordingly, we devise a score that utilizes the information from the cost matrix and warping path alignments. We subsequently consider its fusion (using a sum rule combiner) with the DTW score for authenticating the veracity of a test signature. In addition, a minor modification is suggested with regards to the set of features employed for matching the signatures. We introduce a spacing parameter for feature extraction and demonstrate its applicability in increasing the separation between the distribution of genuine and forgery signatures for an user. Our method has been tested on two publicly available online signature databases namely the SVC-2004 Task 2 and MCYT-100. We report reduction in error rates over the traditional DTW framework.
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Diaz M, Fischer A, Ferrer MA, Plamondon R. Dynamic Signature Verification System Based on One Real Signature. IEEE TRANSACTIONS ON CYBERNETICS 2018; 48:228-239. [PMID: 28114052 DOI: 10.1109/tcyb.2016.2630419] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The dynamic signature is a biometric trait widely used and accepted for verifying a person's identity. Current automatic signature-based biometric systems typically require five, ten, or even more specimens of a person's signature to learn intrapersonal variability sufficient to provide an accurate verification of the individual's identity. To mitigate this drawback, this paper proposes a procedure for training with only a single reference signature. Our strategy consists of duplicating the given signature a number of times and training an automatic signature verifier with each of the resulting signatures. The duplication scheme is based on a sigma lognormal decomposition of the reference signature. Two methods are presented to create human-like duplicated signatures: the first varies the strokes' lognormal parameters (stroke-wise) whereas the second modifies their virtual target points (target-wise). A challenging benchmark, assessed with multiple state-of-the-art automatic signature verifiers and multiple databases, proves the robustness of the system. Experimental results suggest that our system, with a single reference signature, is capable of achieving a similar performance to standard verifiers trained with up to five signature specimens.
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41
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Learning Global-Local Distance Metrics for Signature-Based Biometric Cryptosystems. CRYPTOGRAPHY 2017. [DOI: 10.3390/cryptography1030022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Biometric traits, such as fingerprints, faces and signatures have been employed in bio-cryptosystems to secure cryptographic keys within digital security schemes. Reliable implementations of these systems employ error correction codes formulated as simple distance thresholds, although they may not effectively model the complex variability of behavioral biometrics like signatures. In this paper, a Global-Local Distance Metric (GLDM) framework is proposed to learn cost-effective distance metrics, which reduce within-class variability and augment between-class variability, so that simple error correction thresholds of bio-cryptosystems provide high classification accuracy. First, a large number of samples from a development dataset are used to train a global distance metric that differentiates within-class from between-class samples of the population. Then, once user-specific samples are available for enrollment, the global metric is tuned to a local user-specific one. Proof-of-concept experiments on two reference offline signature databases confirm the viability of the proposed approach. Distance metrics are produced based on concise signature representations consisting of about 20 features and a single prototype. A signature-based bio-cryptosystem is designed using the produced metrics and has shown average classification error rates of about 7% and 17% for the PUCPR and the GPDS-300 databases, respectively. This level of performance is comparable to that obtained with complex state-of-the-art classifiers.
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Morales A, Morocho D, Fierrez J, Vera‐Rodriguez R. Signature authentication based on human intervention: performance and complementarity with automatic systems. IET BIOMETRICS 2017. [DOI: 10.1049/iet-bmt.2016.0115] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Aythami Morales
- BiDA Lab – Biometrics and Data Pattern Analytics LaboratoryUniversidad Autonoma de MadridMadridSpain
| | - Derlin Morocho
- Departamento de Electrica y ElectronicaUniversidad de las Fuerzas Armadas‐ESPESangolquíEcuador
| | - Julian Fierrez
- BiDA Lab – Biometrics and Data Pattern Analytics LaboratoryUniversidad Autonoma de MadridMadridSpain
| | - Ruben Vera‐Rodriguez
- BiDA Lab – Biometrics and Data Pattern Analytics LaboratoryUniversidad Autonoma de MadridMadridSpain
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43
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Ferrer MA, Diaz M, Carmona-Duarte C, Morales A. A Behavioral Handwriting Model for Static and Dynamic Signature Synthesis. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2017; 39:1041-1053. [PMID: 27333600 DOI: 10.1109/tpami.2016.2582167] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The synthetic generation of static handwritten signatures based on motor equivalence theory has been recently proposed for biometric applications. Motor equivalence divides the human handwriting action into an effector dependent cognitive level and an effector independent motor level. The first level has been suggested by others as an engram, generated through a spatial grid, and the second has been emulated with kinematic filters. Our paper proposes a development of this methodology in which we generate dynamic information and provide a unified comprehensive synthesizer for both static and dynamic signature synthesis. The dynamics are calculated by lognormal sampling of the 8-connected continuous signature trajectory, which includes, as a novelty, the pen-ups. The forgery generation imitates a signature by extracting the most perceptually relevant points of the given genuine signature and interpolating them. The capacity to synthesize both static and dynamic signatures using a unique model is evaluated according to its ability to adapt to the static and dynamic signature inter- and intra-personal variability. Our highly promising results suggest the possibility of using the synthesizer in different areas beyond the generation of unlimited databases for biometric training.
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Tolosana R, Vera-Rodriguez R, Fierrez J, Morales A, Ortega-Garcia J. Benchmarking desktop and mobile handwriting across COTS devices: The e-BioSign biometric database. PLoS One 2017; 12:e0176792. [PMID: 28475590 PMCID: PMC5419513 DOI: 10.1371/journal.pone.0176792] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Accepted: 04/17/2017] [Indexed: 11/18/2022] Open
Abstract
This paper describes the design, acquisition process and baseline evaluation of the new e-BioSign database, which includes dynamic signature and handwriting information. Data is acquired from 5 different COTS devices: three Wacom devices (STU-500, STU-530 and DTU-1031) specifically designed to capture dynamic signatures and handwriting, and two general purpose tablets (Samsung Galaxy Note 10.1 and Samsung ATIV 7). For the two Samsung tablets, data is collected using both pen stylus and also the finger in order to study the performance of signature verification in a mobile scenario. Data was collected in two sessions for 65 subjects, and includes dynamic information of the signature, the full name and alpha numeric sequences. Skilled forgeries were also performed for signatures and full names. We also report a benchmark evaluation based on e-BioSign for person verification under three different real scenarios: 1) intra-device, 2) inter-device, and 3) mixed writing-tool. We have experimented the proposed benchmark using the main existing approaches for signature verification: feature- and time functions-based. As a result, new insights into the problem of signature biometrics in sensor-interoperable scenarios have been obtained, namely: the importance of specific methods for dealing with device interoperability, and the necessity of a deeper analysis on signatures acquired using the finger as the writing tool. This e-BioSign public database allows the research community to: 1) further analyse and develop signature verification systems in realistic scenarios, and 2) investigate towards a better understanding of the nature of the human handwriting when captured using electronic COTS devices in realistic conditions.
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Affiliation(s)
- Ruben Tolosana
- Biometrics and Data Pattern Analytics (BiDA) Lab - ATVS, Universidad Autonoma de Madrid, Madrid, Spain
| | - Ruben Vera-Rodriguez
- Biometrics and Data Pattern Analytics (BiDA) Lab - ATVS, Universidad Autonoma de Madrid, Madrid, Spain
| | - Julian Fierrez
- Biometrics and Data Pattern Analytics (BiDA) Lab - ATVS, Universidad Autonoma de Madrid, Madrid, Spain
| | - Aythami Morales
- Biometrics and Data Pattern Analytics (BiDA) Lab - ATVS, Universidad Autonoma de Madrid, Madrid, Spain
| | - Javier Ortega-Garcia
- Biometrics and Data Pattern Analytics (BiDA) Lab - ATVS, Universidad Autonoma de Madrid, Madrid, Spain
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Wen J, Fang B, Zhang L, Zhu Y. Off-Line Signature Verification Based on Multi-Scale Local Structural Pattern. INT J PATTERN RECOGN 2017. [DOI: 10.1142/s0218001417560109] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The paper proposes a theoretically very simple, and effective multi-scale local structural patterns (MS-LSP) for off-line signature verification. The proposed strategy models the signature by probabilistically counting the distribution of each local structural pattern on multiple scales. This representation correlates with the writing style of an individual and depict the shape and structural information of signatures. Extensive experiments on two challenging databases (MCYT, GPDS960) show that the proposed approach achieves noticeably verification rates and it is expected to provide a powerful discriminative representation of the signature.
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Affiliation(s)
- Jing Wen
- College of Computer Science, Chongqing University, Chongqing 400030, P. R. China
- Key Laboratory of Pattern Recognition and Intelligent Information Processing, Institutions of Higher Education of Sichuan Province, Chengdu University, P. R. China
| | - Bin Fang
- College of Computer Science, Chongqing University, Chongqing 400030, P. R. China
| | - Liang Zhang
- College of Computer Science, Chongqing University, Chongqing 400030, P. R. China
| | - Yu Zhu
- College of Computer Science, Chongqing University, Chongqing 400030, P. R. China
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SVM Based Online Signature Verification Technique Using Reference Feature Vector. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES INDIA SECTION A-PHYSICAL SCIENCES 2017. [DOI: 10.1007/s40010-016-0293-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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47
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Abstract
Writer recognition is to identify a person on the basis of handwriting, and great progress has been achieved in the past decades. In this paper, we concentrate ourselves on the issue of off-line text-independent writer recognition by summarizing the state of the art methods from the perspectives of feature extraction and classification. We also exhibit some public datasets and compare the performance of the existing prominent methods. The comparison demonstrates that the performance of the methods based on frequency domain features decreases seriously when the number of writers becomes larger, and that spatial distribution features are superior to both frequency domain features and shape features in capturing the individual traits.
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Affiliation(s)
- Yu-Jie Xiong
- Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and Technology, East China Normal University, Shanghai 200241, P. R. China
| | - Yue Lu
- Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and Technology, East China Normal University, Shanghai 200241, P. R. China
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48
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Loka H, Zois E, Economou G. Long range correlation of preceded pixels relations and application to off‐line signature verification. IET BIOMETRICS 2017. [DOI: 10.1049/iet-bmt.2016.0046] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Hans Loka
- Electronics Engineering DepartmentTechnological and Educational Institute of AthensAgiou Spiridonos Str.12243EgaleoGreece
| | - Elias Zois
- Electronics Engineering DepartmentTechnological and Educational Institute of AthensAgiou Spiridonos Str.12243EgaleoGreece
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49
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Syed Ahmad SM, Loo LY, Wan Adnan WA, Md Anwar R. Wavelet Analysis of Resultant Velocity Belonging to Genuine and Forged Signatures. J Forensic Sci 2016; 62:374-381. [PMID: 28000207 DOI: 10.1111/1556-4029.13303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Revised: 05/12/2016] [Accepted: 05/28/2016] [Indexed: 11/26/2022]
Abstract
This study presents a wavelet analysis of resultant velocity features belonging to genuine and forged groups of signature sample. Signatures of individuals were initially classified based on visual human perceptions of their relative sizes, complexities, and legibilities of the genuine counterparts. Then, the resultant velocity was extracted and modeled through wavelet analysis from each sample. The wavelet signal was decomposed into several layers based on maximum overlap discrete wavelet transform (MODWT). Next, the zero crossing rate features were calculated from all the high wavelet sub-bands. A total of seven hypotheses were then tested using a two-way ANOVA testing methodology. Of these, four hypotheses were conducted to test for significance differences between distributions. In addition, three hypotheses were run to provide test for interaction between two factors of signature authentication versus perceived classification. The results demonstrated that both feature distributions belonging to genuine and forged groups of samples cannot be distinguished by themselves. Instead, they were significantly different under the influence of two other inherent factors, namely perceived size and legibility. Such new findings are useful information particularly in providing bases for forensic justifications in establishing the authenticity of handwritten signature specimens.
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Affiliation(s)
| | - Ling Yim Loo
- College of Information Technology, Universiti Tenaga Nasional (UNITEN), Selangor, 43000, Malaysia
| | - Wan Azizun Wan Adnan
- Faculty of Engineering, Universiti Putra Malaysia (UPM), Selangor, 43400, Malaysia
| | - Rina Md Anwar
- College of Information Technology, Universiti Tenaga Nasional (UNITEN), Selangor, 43000, Malaysia
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Rosso OA, Ospina R, Frery AC. Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers. PLoS One 2016; 11:e0166868. [PMID: 27907014 PMCID: PMC5131934 DOI: 10.1371/journal.pone.0166868] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2016] [Accepted: 11/05/2016] [Indexed: 11/19/2022] Open
Abstract
We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results are better than state-of-the-art online techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups.
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Affiliation(s)
- Osvaldo A. Rosso
- Instituto de Física, Universidade Federal de Alagoas (UFAL), Maceió, AL, Brazil
- Instituto Tecnológico de Buenos Aires (ITBA), and CONICET, Ciudad Autónoma de Buenos Aires, Argentina
- Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago, Chile
- * E-mail:
| | - Raydonal Ospina
- Centro de Ciências Exatas e da Natureza, Departamento de Estatística, Universidade Federal de Pernambuco (UFPE), Recife, PE, Brasil
| | - Alejandro C. Frery
- Laboratório de Computação Científica e Análise Numérica, Universidade Federal de Alagoas (UFAL), Maceió, AL, Brazil
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