Combining fingerprint and hand-geometry verification decisions

Kar Ann Toh, Wei Xiong, Wei Yun Yau, Xudong Jiang

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper proposes to combine the fingerprint and handgeometry verification decisions using a reduced multivariate polynomials model. Main advantage of this method over those neural network based methods is that only a single step is required for training and the training is optimal. Numerical experiments using a database containing over 100 identities show significant improvement of Receiver Operating Characteristics as compared to that of individual biometrics. Moreover, the result outperforms a few commonly used methods using the same database.

Original languageEnglish
Pages (from-to)688-696
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2688
Publication statusPublished - 2003 Dec 1

Fingerprint

Fingerprint
Geometry
Biometrics
Polynomial Model
Multivariate Polynomials
Operating Characteristics
Multivariate Models
Neural networks
Receiver
Numerical Experiment
Neural Networks
Experiments
Training
Statistical Models

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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Combining fingerprint and hand-geometry verification decisions. / Toh, Kar Ann; Xiong, Wei; Yau, Wei Yun; Jiang, Xudong.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2688, 01.12.2003, p. 688-696.

Research output: Contribution to journalArticle

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