Practical On-Line Signature Verification

Pascual-Gaspar, J. M.; Cardeñoso-Payo, Valentı́n; Vivaracho-Pascual, Carlos

doi:10.1007/978-3-642-01793-3_119

Cited by 28 publications

(7 citation statements)

References 19 publications

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“…For example, LNPS may be applied to online character recognition or text recognition. Second, we Method EER (%) Model Yeung et al [22] 5.50 DTW Pascual-Gaspar et al [26] 3.38 DTW Fierrez el at. [6] 6.90 HMM Van et al [7] 4.83 HMM Sharma et al [4] 2.73 DTW Our method 2.37 RNN+LNPS propose a novel RNN system for online signature verification that employs metric learning techniques and a joint training scheme.…”

Section: Discussionmentioning

confidence: 99%

Online Signature Verification Using Recurrent Neural Network and Length-Normalized Path Signature Descriptor

Lai

Jin

Yang

2017

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

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Inspired by the great success of recurrent neural networks (RNNs) in sequential modeling, we introduce a novel RNN system to improve the performance of online signature verification. The training objective is to directly minimize intra-class variations and to push the distances between skilled forgeries and genuine samples above a given threshold. By back-propagating the training signals, our RNN network produced discriminative features with desired metrics. Additionally, we propose a novel descriptor, called the length-normalized path signature (LNPS), and apply it to online signature verification. LNPS has interesting properties, such as scale invariance and rotation invariance after linear combination, and shows promising results in online signature verification. Experiments on the publicly available SVC-2004 dataset yielded state-of-the-art performance of 2.37% equal error rate (EER).

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Section: Discussionmentioning

confidence: 99%

Online Signature Verification Using Recurrent Neural Network and Length-Normalized Path Signature Descriptor

Lai

Jin

Yang

2017

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

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“…This scheme can be applied to more sophisticated online signature verification algorithms such as [25], [26]. Thus, our future projects will include evaluation of the scheme in such algorithms.…”

Section: Discussionmentioning

confidence: 99%

Hill-Climbing Attacks and Robust Online Signature Verification Algorithm against Hill-Climbing Attacks

Muramatsu

2010

IEICE Trans. Inf. & Syst.

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SUMMARYAttacks using hill-climbing methods have been reported as a vulnerability of biometric authentication systems. In this paper, we propose a robust online signature verification algorithm against such attacks. Specifically, the attack considered in this paper is a hill-climbing forged data attack. Artificial forgeries are generated offline by using the hill-climbing method, and the forgeries are input to a target system to be attacked. In this paper, we analyze the menace of hill-climbing forged data attacks using six types of hill-climbing forged data and propose a robust algorithm by incorporating the hill-climbing method into an online signature verification algorithm. Experiments to evaluate the proposed system were performed using a public online signature database. The proposed algorithm showed improved performance against this kind of attack. key words: hill-climbing attack, online signature verification, biometrics, vulnerability, offline attack IntroductionBiometric person authentication technologies are becoming more important in the drive to ensure security. These technologies are being actively studied, and some of them are being used in real situations. However, several vulnerabilities have been reported [1], [2], including a hill-climbing attack [3]. Hill-climbing attacks against biometric systems of several modalities, such as face [4]- [7], fingerprints [8], [9], and online signatures [10], [11] have been reported.There are two types of hill-climbing attack: 1. Online hill-climbing attack: Attackers access the targeted biometric system directly and attack it repeatedly [4]- [11]. 2. Offline hill-climbing attack (hill-climbing forged data attack): Attackers repeatedly access a physically different biometric system from the targeted biometric system and generate hill-climbing forged data. Then, the forged data is input to the targeted biometric system. Several countermeasures against online attacks have been reported, for example, limiting the number of sequential attempts [3], score transformation [12], and so on. Also, a parameter-updating method [13], [14] is one possible solution. However, these measures are not useful against an offline attack because attackers do not need to access the targeted biometric system repeatedly; rather, they input forged data to the targeted biometric system only once. Thus, different countermeasures are necessary against an offline at- tack. However, no useful countermeasures for any type of modality have been proposed. In this paper, we propose a countermeasure against offline hill-climbing attacks for online signature verification. A robust online signature verification algorithm against such attacks was implemented by incorporating a hill-climbing algorithm into a verification algorithm.Experiments were performed using a public online signature database, SVC2004 [15]. Experimental results show that the proposed algorithm had improved performance against hill-climbing forged data attacks.We first describe a basic online signature verification algorithm in S...

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“…The algorithm error rates in previous experiments with portable devices were EER = 1.8% for random forgeries and EER = 7.6% for skilled forgeries. As the algorithm performance is out of the topic of this research, a detailed description and the previous results obtained are not included, though can be accessed via the referenced paper [9]. The biometric system performance results will be given through the EER, which even not offering the complete information about the results, offers a good trade-off between the FAR and FRR, measure enough for comparing the accuracy between the experiments.…”

Section: Algorithm Detailsmentioning

confidence: 98%

“…The system analyzed uses a DTW-based handwritten signature recognition algorithm [9] in mobile scenarios. The experiment was made under a scenario evaluation and the users were invited to sign in four different devices, in five different positions and in three sessions separated one week each.…”

Section: Introductionmentioning

confidence: 99%

Usability analysis of a handwritten signature recognition system applied to mobile scenarios

Blanco-Gonzalo

Miguel-Hurtado

Sánchez-Reillo

et al. 2013

2013 47th International Carnahan Conference on Security Technology (ICCST)

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Along with the necessity to solve the problems due to the misuse of biometric systems and thus the consistent increase in the final products error rates, a usability evaluation on handwritten signature recognition was carried out. Furthermore, according to the popularity of mobile devices and the market trends, the evaluation was performed signing in mobile scenarios with smart phones, tablets and other common mobile devices. This study reveals interesting results correlating habituation and preferences with better or worst results and it shows the need of involve the user more incisively in the development of biometric solutions, not only for comfort issues but for better systems throughput

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Practical On-Line Signature Verification

Cited by 28 publications

References 19 publications

Online Signature Verification Using Recurrent Neural Network and Length-Normalized Path Signature Descriptor

Online Signature Verification Using Recurrent Neural Network and Length-Normalized Path Signature Descriptor

Hill-Climbing Attacks and Robust Online Signature Verification Algorithm against Hill-Climbing Attacks

Usability analysis of a handwritten signature recognition system applied to mobile scenarios

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