Handwriting verification – Comparison of a multi-algorithmic and a multi-semantic approach

Scheidat, Tobias; Vielhauer, Claus; Dittman, Jana

doi:10.1016/j.imavis.2007.03.006

Cited by 11 publications

(8 citation statements)

References 12 publications

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“…The multi-algorithm approach only led to some slight improvements on the decision level, whereas on the other levels even worse results than without any fusion at all were obtained. This contrasts to the biometrics domain, where also for multi-algorithm approaches improved results occur [12].…”

Section: Discussionmentioning

confidence: 89%

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Evaluation of Fusion for Similarity Searching in Online Handwritten Documents

Schimke

Schott

Vielhauer

et al. 2009

Advances in Data Mining. Applications and Theoretical Aspects

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Abstract. With the spread of TabletPCs handwriting raises in its significance and importance in the digital domain. Also there exist other devices with penbased inputs like PDAs, digitizer tablets and pads specially prepared with sensors. The advantage of handwritten input methods is their possibility of an ad hoc creation of technical sketches and drawings alongside with text and that keyboards may be in some cases and environments bothersome. Therefore the amount of handwritten documents is likely to increase. But a great problem is a proper full text search on such documents. This paper discusses the effects of multi-sample and multi-algorithm fusion approaches, known from biometrics to increase the performance. The tests are done by using three different devices (Logitech ioPen, Pegasus PC NotesMaker, ACE CAD DigiMemo Digital) and five different feature extraction methods (square grid, triangular grid, slope, curvature and slant of writing) and show that fusion can improve the retrieval performance in terms of precision and recall from 0.903 and 0.935 without fusion to 0.958 and 0.943 with fusion, respectively.

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Section: Discussionmentioning

confidence: 89%

“…Handwritten data can be either textual and use some specially developed simple alphabets, a standard alphabet of a language or a cursive. Other types are gestures to give commands to the computer, symbols and drawings, or signatures, which are used in biometrics for the authentication of persons [12].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Fusion for Similarity Searching in Online Handwritten Documents

Schimke

Schott

Vielhauer

et al. 2009

Advances in Data Mining. Applications and Theoretical Aspects

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“…The weights are selected such that to satisfy condition w X + w Y + w Z = 1. Previously, in other biometric studies, computing weights based on EER values of individual modalities shown to be useful [36,37]. For the four aforementioned combinations, weights were also computed using EER values as follow 1.…”

Section: Results With Fusionmentioning

confidence: 99%

User authentication based on foot motion

Gafurov

Bours

Snekkenes

2011

SIViP

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In nearly all current systems, user authentication mechanism is one time and static. Although such type of user authentication is sufficient for many types of applications, in some scenarios, continuous or periodic re-verification of the identity is desirable, especially in high-security application. In this paper, we study user authentication based on 3D foot motion, which can be suitable for periodic identity re-verification purposes. Three-directional (3D) motion of the foot (in terms of acceleration signals) is collected using a wearable accelerometer sensor attached to the ankle of the person. Ankle accelerations from three directions (up-down, forward-backward and sideways) are analyzed for person authentication. Applied recognition method is based on detecting individual cycles in the signal and then finding best matching cycle pair between two acceleration signals. Using experimental data from 30 subjects, obtained EERs (Equal Error Rates) were in the range of 1.6-23.7% depending on motion directions and shoe types. Furthermore, by combining acceleration signals from 2D and 3D and then applying fusing techniques, recognition accuracies could be improved even further. The achieved performance improvements (in terms of EER) were up to 68.8%.

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“…The weights of matching scores are determined by dividing the single EER with sum of all EER (see equation 3). A property of this weighting scheme is that the matching scores of the system, which received the highest EER are multiplied with the smallest weight and vice versa.…”

Section: Linear Weighted Fusionmentioning

confidence: 99%

Single-Semantic Multi-Instance Fusion of Handwriting Based Biometric Authentication Systems

Scheidat

Vielhauer

Dittmann

2007

2007 IEEE International Conference on Image Processing

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The fusion of biometric systems, algorithms and/or traits is a well known solution to improve authentication performance of biometric systems. In this article the fusion of two instances of the same semantic is suggested, where semantics are alternative handwritten contents such as numbers or sentences, in addition to commonly used signature. In order to fuse two instances of one semantic, a biometric authentication is carried out on both by Biometric Hash algorithm up to matching score computation. The fusion is done by combination of matching scores to a joint score as basis for authentication decision. Three individual fusion strategies are used to study to which degree the authentication performance can be improved or degraded. Therefore one pragmatic and two optimistically weighting approaches for biometric fusion are used. The best fusion result is even better than the corresponding best individual result by approximately 17%.

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Handwriting verification – Comparison of a multi-algorithmic and a multi-semantic approach

Cited by 11 publications

References 12 publications

Evaluation of Fusion for Similarity Searching in Online Handwritten Documents

Evaluation of Fusion for Similarity Searching in Online Handwritten Documents

User authentication based on foot motion

Single-Semantic Multi-Instance Fusion of Handwriting Based Biometric Authentication Systems

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