A Flexible Architecture for Online Signature Verification Based on a Novel Biometric Pen

Handwritten signature recognition is still the most widely accepted method to validate paper based documents. However, in the digital world, there is no readymade way to distinguish a real handwritten signature on a scanned document from a forged copy of another signature made by the same person on another document that is simply "pasted" into the forged document. In this paper we describe how we are using the touch screen of smartphones or tablets to collect handwritten signature images and associated biometric markers derived from the motion direction of handwritten signatures that are made directly into the device touchscreen. These time base biometric markers can then be converted into signaling time waves, by using the dragging or lifting movements the user makes with a touch screen omnidirectional tip stylus, when he handwrites is signature at the device touchscreen. These time/space signaling time waves can then be converted into a biometric bit stream that can be matched with previously enrolled biometric markers of the user's handwritten signature. In this paper we contend that the collection of these simple biometric features is sufficient to achieve a level of user recognition and authentication that is sufficient for the majority of online user authentication and digital documents authenticity.

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“…Our focus is on online authentication because it offers a much higher security level than offline signature verification [2].…”

Section: A Types Of Verificationmentioning

confidence: 99%

Time/space based biometric handwritten signature verification

Goncalves

Augusto²,

Correia³

2015

2015 10th Iberian Conference on Information Systems and Technologies (CISTI)

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“…Normalization Generally, in DTW based classifiers, the sequences are normalized in time and/or amplitude domains [6,[10][11][12][15][16][17]. Different length and amplitude normalization procedures on time series obtained from handwritten PIN words and signatures sequences are described in the sections below.…”

Section: Sum Of Multivariate Time Seriesmentioning

confidence: 99%

“…Generally, in the signature verification literature norm 2 is dominantly used for amplitude normalization [6,[15][16][17][18]. In this paper, three normalization of norm 1 , norm 2 and norm 3 are investigated.…”

Section: Amplitude Normalization Of Time Seriesmentioning

confidence: 99%

DTW Based Classification of Diverse Pre-Processed Time Series Obtained from Handwritten PIN Words and Signatures

Bashir

Kempf

2010

J Sign Process Syst

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Personal identity verification by means of signature handwriting dynamics is a widely researched aspect of behavioral biometrics. The Dynamic Time Warping (DTW) technique has been successfully used for accessing the similarity of time series of handwritten objects by minimizing non-linear time distortions. Generally, in DTW based classifiers, the sequences are normalized in time and amplitude domains. In the paper, different length and amplitude normalization techniques are applied on signatures and handwritten PIN word sequences and their influence on accuracy of recognition are examined. A special approach to amplitude normalization based on reference level assigned Dynamic Time Warping (DTW) technique is presented. The standard deviation values calculated from the time series are used as so called bio-reference levels to improve the performance of classification. For this, they are added to the time series of query and sample datasets prior to DTW matching. The acquisition of online data is carried out by a digital pen equipped with pressure and inclination sensors. The time series obtained from the pen during handwriting provide valuable insight into the unique characteristics of the writers. Experimental results show that with the help of proposed length and amplitude normalizations of sequences including the bio-reference levels, the computational time is reduced and false acceptance rates are decreased.

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“…Since we are developing digital pen to recognize characters, we also interested to provide authentication through the same pen. In order to do literature survey related to this researches such as Gruber, C [17] and Bashir M [18] have proposed online signature verification based on novel biometric pen using the ensemble techniques based on the finger grip pressure, acceleration, and tilt data using the ensemble techniques and dynamic time wrapping (DTW) algorithm respectively. Sayed B [19] have proposed mouse dynamic biometric authentication which performs static authentication in short time.…”

Section: Introductionmentioning

confidence: 99%

Online hand written character recognition using Digital Pen for static authentication

Renuka

Suganya

2014

2014 International Conference on Computer Communication and Informatics

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Due to the rapid development of computer technology, Human Computer Interaction (HCI) techniques have become an indispensable component in our daily life. HCI focuses on the understanding between humans and the computer. Also the protection of password involves different methods and it is being a great challenge to the computer industry. Hand Written Character is one form of human interaction can be used effectively in HCI for static authentication with better password protection. In this paper, the Digital Pen consists of triaxial micro-electromechanical (MEMS) Accelerometer, to capture the activities of the human and to capture motion trajection information from accelerations for recognizing gestures for handwriting. The pen operates in two modes. It can operate as mouse by recognizing the gestures and other mode it can operate as Handwritten Character Recognition for identifying the digits and characters also in this mode it provides static authentication for the systems by recognizing the characters. An Integrated Development Interface is developed using dotnet to recognize the characters.

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A Flexible Architecture for Online Signature Verification Based on a Novel Biometric Pen

Cited by 14 publications

References 14 publications

Time/space based biometric handwritten signature verification

Time/space based biometric handwritten signature verification

DTW Based Classification of Diverse Pre-Processed Time Series Obtained from Handwritten PIN Words and Signatures

Online hand written character recognition using Digital Pen for static authentication

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