&lt;title&gt;Fusion strategies for speech and handwriting modalities in HCI&lt;/title&gt;

Vielhauer, Claus; Schimke, Sascha; Thanassis, Valsamakis; Stylianou, Yannis

doi:10.1117/12.585869

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…The fusion on matching score level improves the three individual results considerably here. Vielhauer et al present in [12] a multimodal system where a speech recognition system and a signature recognition system are fused on matching score level. In [13] an enhancement of the multimodal system by exchange of the single signature component by the multi-algorithmic handwriting subsystem proposed in [1] is suggested.…”

Section: (4) Multiple Biometric Traitsmentioning

confidence: 99%

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

Scheidat¹,

Vielhauer²,

Dittman³

2009

Image and Vision Computing

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Section: (4) Multiple Biometric Traitsmentioning

confidence: 99%

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

Scheidat¹,

Vielhauer²,

Dittman³

2009

Image and Vision Computing

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“…Jain and Ross describe in [1] an improvement by a multimodal fusion using face, fingerprint and hand geometry. In [2] Vielhauer et al present a multimodal system where a speech recognition system and a signature recognition system are fused in order to obtain a better authentication result in comparison to the single biometric systems involved. An enhancement of this multimodal system was suggested in [3] by exchange of the single signature component by a multi-algorithmic handwriting subsystem.…”

Section: Introductionmentioning

confidence: 99%

“…An enhancement of this multimodal system was suggested in [3] by exchange of the single signature component by a multi-algorithmic handwriting subsystem. By this multimodal/multi-algorithmic fusion an improvement of 15% could achieved in comparison to original multimodal system described in [2]. The multi-algorithmic method is proposed in [4] by Scheidat et al and uses a combination of four signature verification algorithms in order to improve the verification result.…”

Section: Introductionmentioning

confidence: 99%

Analyzing a multimodal biometric system using real and virtual users

Scheidat

Vielhauer

2007

SPIE Proceedings

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Three main topics of recent research on multimodal biometric systems are addressed in this article: The lack of sufficiently large multimodal test data sets, the influence of cultural aspects and data protection issues of multimodal biometric data. In this contribution, different possibilities are presented to extend multimodal databases by generating so-called virtual users, which are created by combining single biometric modality data of different users. Comparative tests on databases containing real and virtual users based on a multimodal system using handwriting and speech are presented, to study to which degree the use of virtual multimodal databases allows conclusions with respect to recognition accuracy in comparison to real multimodal data. All tests have been carried out on databases created from donations from three different nationality groups. This allows to review the experimental results both in general and in context of cultural origin. The results show that in most cases the usage of virtual persons leads to lower accuracy than the usage of real users in terms of the measurement applied: the Equal Error Rate. Finally, this article will address the general question how the concept of virtual users may influence the data protection requirements for multimodal evaluation databases in the future.

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“…The speech trait is comprised of two main components as shown in figure 5: speech feature extraction and a Gaussian Mixture Model (GMM) classifier. The speech signal is analyzed on a frame by frame basis, with a typical frame length of 20 ms and a frame advance of 10 ms [16]. For each frame, a dimensional feature vector is extracted, the discrete Fourier spectrum is obtained via a fast Fourier transform from which magnitude squared spectrum is computed and put it through a bank of filters.…”

Section: Voice Analysis and Feature Extractionmentioning

confidence: 99%

Product of Likelihood Ratio Scores Fusion of Face, Speech and Signature Based FJ-GMM for Biometrics Authentication Application Systems

Sarwat¹

2017

MCS

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Abstract:The paper proposes a likelihood ratio fusion of face, voice and signature multimodal biometrics verification application systems. Figueiredo-Jain (FJ) estimation algorithm of finite Gaussian mixture modal (GMM) is employed. Automated biometric systems for human identification measure a "signature" of the human body, compare the resulting characteristic to a database, and render an application dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are typically distinctive, Multi-biometric systems, which consolidate information from multiple biometric sources, are gaining popularity because they are able to overcome limitations such as non-universality, noisy sensor data, large intra-user variations and susceptibility to spoof attacks that are commonly encountered in mono modal biometric systems. Simulation show that finite mixture modal (GMM) is quite effective in modelling the genuine and impostor score densities, fusion based the resulting density estimates achieves a significant performance on eNTERFACE 2005 multi-modal database based on face, signature and voice modalities.

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<title>Fusion strategies for speech and handwriting modalities in HCI</title>

Cited by 8 publications

References 14 publications

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

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

Analyzing a multimodal biometric system using real and virtual users

Product of Likelihood Ratio Scores Fusion of Face, Speech and Signature Based FJ-GMM for Biometrics Authentication Application Systems

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