On-line signature verification based on altitude and direction of pen movement

Hangai, Seiichiro; Yamanaka, S.; Hanamoto, Tsuyoshi

doi:10.1109/icme.2000.869645

Cited by 32 publications

(15 citation statements)

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“…, N is the discrete time index given by the acquisition device and N is the time duration of the signature in sampling units. Although pen inclination trajectories have shown some discriminative capabilities in other works (Hangai et al, 2000;Sakamoto et al, 2001;Pacut and Czajka, 2001), the use of these two functions in our system degrades the verification performance (as it is shown in Section 4.4.1). As a result, the basic function set consists of x n , y n and p n .…”

Section: Basic Functionsmentioning

confidence: 93%

HMM-based on-line signature verification: Feature extraction and signature modeling

Fiérrez-Aguilar

Ortega‐Garcia

Ramos

et al. 2007

Pattern Recognition Letters

280

118

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Section: Basic Functionsmentioning

confidence: 93%

HMM-based on-line signature verification: Feature extraction and signature modeling

Fiérrez-Aguilar

Ortega‐Garcia

Ramos

et al. 2007

Pattern Recognition Letters

280

118

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“…As a result, some researchers have stopped using pen pressure, [15], pen altitude, and pen azimuth [16] or have expressed concern about using pen inclination features [17]. On the other hand, some researchers have proposed algorithms taking account of pen inclination features and reported improved performance [18]- [20]. We also reported improved performance using pen pressure and inclination features [14].…”

Section: Introductionmentioning

confidence: 64%

Effectiveness of Pen Pressure, Azimuth, and Altitude Features for Online Signature Verification

Muramatsu

Matsumoto

Lecture Notes in Computer Science

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Abstract. Many algorithms for online signature verification using multiple features have been proposed. Recently it has been argued that pen pressure, azimuth, and altitude can cause instability and deteriorate the performance. Algorithms without pen pressure and inclination features outperformed with them in SVC2004. However, we previously found that these features improved the performance in evaluations using our private database. The effectiveness of the features thus depended on the algorithm. Therefore, we re-evaluated our algorithm using the same database as used in SVC2004 and discuss the effectiveness of pen pressure, azimuth and altitude. Experimental results show that even though these features are not so effective when they are used by themselves, they improved the performance when used in combination with other features. When pen pressure and inclination features were considered, an EER of 3.61% was achieved, compared to an EER of 5.79% when these features were not used.

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“…The pressure measure is either taken at the pen tip [2,14] or by four sensors in each corner of the writing tablet [5]. However, the underlying authentication algorithms are proprietary and cannot be evaluated.…”

Section: Writing Pressure Signal P(t)mentioning

confidence: 99%

“…Besides handwriting digitizing devices that deliver signals for writing position and pressure, new approaches are described in [10,5]. Signals are being sampled using either force / acceleration sensors [10] or by calculating the pen angle using pen height and position at the pen top [5].…”

Section: Writing Pen Angle Signal ϕ(T)mentioning

confidence: 99%

<title>Approaches to biometric watermarks for owner authentification</title>

Vielhauer

Steinmetz

2001

SPIE Proceedings

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One major application domain for digital watermarks is copyright protection. Besides the design of watermarking algorithms, technologies for copyright holder identification have to be investigated. To ensure authenticity of an individual person, a wide number of biometric procedures exist. We define and describe new biometric watermarks, which denote the application of biometric reference data of individuals within digital watermarks to identify and verify ownership. Amongst the two classes of physiological and senso-motoric biometric schemes, the later appears more appropriate for biometric watermarks, as only these provide implicit expressions of intention. As such, we choose on-line handwriting as an appropriate base technology for our three new scenarios in biometric watermarking. In the first approach, embedding keys are being generated from biometric reference data, which requires stable and robust features and leads to rather complex keys. To overcome the complexity boundaries, the second approach develops a biometric reference hash, allowing key look-ups in key certifying servers. Although this proceeding leads to less complex keys, it still requires stable features. The third approach describes the embedding of biometric reference data within a watermark, allowing owner verification by more variant features, but limitations apply due to capacity of watermarking systems and also protection of the reference data is required. While most handwriting-based verification systems are limited to signature contexts, we discuss two additional context types for user authentication: passphrases and sketches.

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On-line signature verification based on altitude and direction of pen movement

Cited by 32 publications

References 0 publications

HMM-based on-line signature verification: Feature extraction and signature modeling

HMM-based on-line signature verification: Feature extraction and signature modeling

Effectiveness of Pen Pressure, Azimuth, and Altitude Features for Online Signature Verification

<title>Approaches to biometric watermarks for owner authentification</title>

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