Review: Decision dependability and its application to identity management

Egon van den Broek

Research output: Contribution to journalBook/Film/Article reviewAcademic

Abstract

Kalka, Bartlow, and Cukic adopt the work of Kryszczuk and Drygajlo on unimodal and bimodal biometric classification [1], and present a rather straightforward extension to multimodal classification. In their 2008 paper [1], Kryszczuk and Drygajlo employ a subjective Bayesian methodology to determine the credibility of a decision. They consider a single event probability as a degree of belief in the occurrence of that event. They demonstrate that such credence estimates can help both in predicting and rectifying verification errors. Consequently, the classification performance can be improved. Moreover, they show in a small experiment that their technique can also be of use for fusion of verification decisions, using two modalities. Kalka, Bartlow, and Cukic’s results show marginal improvement in classification performance. More than anything else, their paper illustrates the complexity of multimodal biometric classification. As it is often shown, sensors’ multimodal biometric information processing cannot keep up with the rapid developments in sensor technology. 1) Kryszczuk, K.; Drygajlo, A. Credence estimation and error prediction in biometric identity verification. Signal Processing 88, 4 (2008), 916–925.
Original languageUndefined
Pages (from-to)CR137554
Number of pages1
JournalComputing reviews
Publication statusPublished - 8 Dec 2009

Keywords

  • EWI-18400
  • Bayesian methodology
  • Review
  • Biometrics
  • Multimodal
  • HMI-IE: Information Engineering

Cite this

@article{6f6792fa34054f0bb7667e0ebe69a356,
title = "Review: Decision dependability and its application to identity management",
abstract = "Kalka, Bartlow, and Cukic adopt the work of Kryszczuk and Drygajlo on unimodal and bimodal biometric classification [1], and present a rather straightforward extension to multimodal classification. In their 2008 paper [1], Kryszczuk and Drygajlo employ a subjective Bayesian methodology to determine the credibility of a decision. They consider a single event probability as a degree of belief in the occurrence of that event. They demonstrate that such credence estimates can help both in predicting and rectifying verification errors. Consequently, the classification performance can be improved. Moreover, they show in a small experiment that their technique can also be of use for fusion of verification decisions, using two modalities. Kalka, Bartlow, and Cukic’s results show marginal improvement in classification performance. More than anything else, their paper illustrates the complexity of multimodal biometric classification. As it is often shown, sensors’ multimodal biometric information processing cannot keep up with the rapid developments in sensor technology. 1) Kryszczuk, K.; Drygajlo, A. Credence estimation and error prediction in biometric identity verification. Signal Processing 88, 4 (2008), 916–925.",
keywords = "EWI-18400, Bayesian methodology, Review, Biometrics, Multimodal, HMI-IE: Information Engineering",
author = "{van den Broek}, Egon",
year = "2009",
month = "12",
day = "8",
language = "Undefined",
pages = "CR137554",
journal = "Computing reviews",
issn = "0010-4884",
publisher = "Association for Computing Machinery (ACM)",

}

Review: Decision dependability and its application to identity management. / van den Broek, Egon.

In: Computing reviews, 08.12.2009, p. CR137554.

Research output: Contribution to journalBook/Film/Article reviewAcademic

TY - JOUR

T1 - Review: Decision dependability and its application to identity management

AU - van den Broek, Egon

PY - 2009/12/8

Y1 - 2009/12/8

N2 - Kalka, Bartlow, and Cukic adopt the work of Kryszczuk and Drygajlo on unimodal and bimodal biometric classification [1], and present a rather straightforward extension to multimodal classification. In their 2008 paper [1], Kryszczuk and Drygajlo employ a subjective Bayesian methodology to determine the credibility of a decision. They consider a single event probability as a degree of belief in the occurrence of that event. They demonstrate that such credence estimates can help both in predicting and rectifying verification errors. Consequently, the classification performance can be improved. Moreover, they show in a small experiment that their technique can also be of use for fusion of verification decisions, using two modalities. Kalka, Bartlow, and Cukic’s results show marginal improvement in classification performance. More than anything else, their paper illustrates the complexity of multimodal biometric classification. As it is often shown, sensors’ multimodal biometric information processing cannot keep up with the rapid developments in sensor technology. 1) Kryszczuk, K.; Drygajlo, A. Credence estimation and error prediction in biometric identity verification. Signal Processing 88, 4 (2008), 916–925.

AB - Kalka, Bartlow, and Cukic adopt the work of Kryszczuk and Drygajlo on unimodal and bimodal biometric classification [1], and present a rather straightforward extension to multimodal classification. In their 2008 paper [1], Kryszczuk and Drygajlo employ a subjective Bayesian methodology to determine the credibility of a decision. They consider a single event probability as a degree of belief in the occurrence of that event. They demonstrate that such credence estimates can help both in predicting and rectifying verification errors. Consequently, the classification performance can be improved. Moreover, they show in a small experiment that their technique can also be of use for fusion of verification decisions, using two modalities. Kalka, Bartlow, and Cukic’s results show marginal improvement in classification performance. More than anything else, their paper illustrates the complexity of multimodal biometric classification. As it is often shown, sensors’ multimodal biometric information processing cannot keep up with the rapid developments in sensor technology. 1) Kryszczuk, K.; Drygajlo, A. Credence estimation and error prediction in biometric identity verification. Signal Processing 88, 4 (2008), 916–925.

KW - EWI-18400

KW - Bayesian methodology

KW - Review

KW - Biometrics

KW - Multimodal

KW - HMI-IE: Information Engineering

M3 - Book/Film/Article review

SP - CR137554

JO - Computing reviews

JF - Computing reviews

SN - 0010-4884

ER -