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Personalized Face Reference from Video: Key-Face Selection and Feature-Level Fusion

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Face and Facial Expression Recognition from Real World Videos (FFER 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8912))

Abstract

Face recognition from video in uncontrolled environments is an active research field that received a growing attention recently. This was mainly driven by the wide range of applications and the availability of large databases. This work presents an approach to create a robust and discriminant reference face model from video enrollment data. The work focuses on two issues, first is the key faces selection from video sequences. The second is the feature-level fusion of the key faces. The proposed fusion approaches focus on inducing subject specific feature weighting in the reference face model. Quality based sample weighting is also considered in the fusion process. The proposed approach is evaluated under different sittings on the YouTube Faces data-base and the performance gained by the proposed approach is shown in the form of EER values and ROC curves.

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Authors and Affiliations

  1. Fraunhofer Institute for Computer Graphics Research (IGD), Darmstadt, Germany

    Naser Damer, Timotheos Samartzidis & Alexander Nouak

Authors
  1. Naser Damer
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  2. Timotheos Samartzidis
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  3. Alexander Nouak
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Corresponding author

Correspondence to Naser Damer .

Editor information

Editors and Affiliations

  1. Computer and Systems Engineering, Pensselaer Polytechnic Institute, Troy, New York, USA

    Qiang Ji

  2. Department of Architecture, Design, and Media Technology, Aalborg, Denmark

    Thomas B. Moeslund

  3. Department of Computer Science, Stevens Institute of Technology, Hoboken, New Jersey, USA

    Gang Hua

  4. Department of Architecture, Design, and Media Technology, Aalborg, Denmark

    Kamal Nasrollahi

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© 2015 Springer International Publishing Switzerland

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Damer, N., Samartzidis, T., Nouak, A. (2015). Personalized Face Reference from Video: Key-Face Selection and Feature-Level Fusion. In: Ji, Q., B. Moeslund, T., Hua, G., Nasrollahi, K. (eds) Face and Facial Expression Recognition from Real World Videos. FFER 2014. Lecture Notes in Computer Science(), vol 8912. Springer, Cham. https://doi.org/10.1007/978-3-319-13737-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-13737-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13736-0

  • Online ISBN: 978-3-319-13737-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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