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A SIFT-Based Feature Level Fusion of Iris and Ear Biometrics

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Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction (MPRSS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8869))

Abstract

To overcome the drawbacks encountoured in unimodal biometric systems for person authentification, multimodal biometrics methods are needed. This paper presents an efficient feature level fusion of iris and ear images using SIFT descriptors which extract the iris and ear features separetely. Then, these features are incorporated in a single feature vector called fused template. The generated template is enrolled in the database, then the matching of SIFT features of iris and ear input images and the enrolled template of the claiming user is computed using Euclidean distance. The proposed method has been applied on a synthetic multimodal biometrics database. The latter is produced from Casia and USTB 2 databases wich represent iris and ear image sets respectively. As the performance evaluation of the proposed method we compute the false rejection rate (FRR), the false acceptance rate (FAR) and accuracy measures. From the obtained results, we can say that the fusion at feature level outperforms iris and ear authentification systems taken separately.

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Authors and Affiliations

  1. MISC Laboratory, Constantine 2 University, 25000, Constantine, Algeria

    Lamis Ghoualmi, Salim Chikhi & Amer Draa

Authors
  1. Lamis Ghoualmi
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  2. Salim Chikhi
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  3. Amer Draa
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Corresponding authors

Correspondence to Lamis Ghoualmi , Salim Chikhi or Amer Draa .

Editor information

Editors and Affiliations

  1. University of Ulm, Universität Ulm, Ulm, Germany

    Friedhelm Schwenker

  2. University of Southern California, Playa Vista, California, USA

    Stefan Scherer

  3. University of Southern California, Playa Vista, California, USA

    Louis-Philippe Morency

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Ghoualmi, L., Chikhi, S., Draa, A. (2015). A SIFT-Based Feature Level Fusion of Iris and Ear Biometrics. In: Schwenker, F., Scherer, S., Morency, LP. (eds) Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction. MPRSS 2014. Lecture Notes in Computer Science(), vol 8869. Springer, Cham. https://doi.org/10.1007/978-3-319-14899-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-14899-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14898-4

  • Online ISBN: 978-3-319-14899-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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