Bimodal biometrics based on a representation and recognition approach

Yong Xu, Aini Zhong, Jian Yang, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

63 Citations (Scopus)

Abstract

It has been demonstrated that multibiometrics can produce higher accuracy than single biometrics. This is mainly because the use of multiple biometric traits of the subject enables more information to be used for identification or verification. In this paper, we focus on bimodal biometrics and propose a novel representation and recognition approach to bimodal biometrics. This approach first denotes the biometric trait sample by a complex vector. Then, it represents the test sample through the training samples and classifies the test sample as follows: let the test sample be expressed as a linear combination of all the training samples each being a complex vector. The proposed approach obtains the solution by solving a linear system. After evaluating the effect, in representing the test sample of each class, the approach classifies the test sample into the class that makes the greatest effect. The approach proposed is not only novel but also simple and computationally efficient. A large number of experiments show that our method can obtain promising results.
Original languageEnglish
Article number037202
JournalOptical Engineering
Volume50
Issue number3
DOIs
Publication statusPublished - 1 Mar 2011

Keywords

  • biometrics
  • computer vision
  • face recognition
  • pattern recognition

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering(all)

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