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Biometric Recognition Systems Using Multispectral Imaging

  • Abdallah Meraoumia
  • Salim Chitroub
  • Ahmed Bouridane
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 70)

Abstract

Automatic personal identification is playing an important role in secure and reliable applications, such as access control, surveillance systems, information systems, physical buildings and many more applications. In contrast with traditional approaches, based on what a person knows (password) or what a person has (tokens), biometric based identification providing an improved security for their users. Biometrics is the measurement of physiological traits such as palmprints, fingerprints, iris etc., and/or behavioral traits such as gait, signature etc., of an individual person for personal recognition. Hand-based person identification provides a good user acceptance, distinctiveness, universality, relatively easy to capture, low-cost and inexpensive. Palmprint identification is one kind of hand-biometric technology and a relatively new biometrics due to its stable and unique traits. The rich texture information of palmprint offers one of the powerful means in personal identification. Several studies for palmprint-based person identification have focused on the use of palmprint images captured in the visible part of the spectral band. However, recently, the multispectral palmprints have been rendered available and the tendency now in the community is how to exploit these multispectral data to improve the performances of the palmprint-based person identification systems. In this chapter, we try to evaluate the usefulness of the multispectral palmprints for improving the palmprint based person identification systems. For that purpose, we propose several systems of exploiting the multispectral palmprints. The results on a medium-size database show good identification performance based on individual modalities as well as after fusing multiple spectral bands.

Keywords

Spectral Band Equal Error Rate Biometric System False Reject Rate Multimodal System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Abdallah Meraoumia
    • 1
  • Salim Chitroub
    • 2
  • Ahmed Bouridane
    • 3
  1. 1.Faculté des nouvelles technologies de l’information et de la communication, Laboratoire de Génie ÉlectriqueUniversité de OuarglaOuarglaAlgeria
  2. 2.Image Processing Laboratory, Electronics and Computer Science FacultyUSTHBAlgiersAlgeria
  3. 3.School of Computing, Engineering and Information SciencesNorthumbria UniversityNewcastle upon TyneUK

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