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Pyramid Mean Representation of Image Sequences for Fast Face Retrieval in Unconstrained Video Data

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Advances in Visual Computing (ISVC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8888))

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Abstract

This paper addresses the problem of face retrieval on large datasets by proposing an efficient representation for face videos. In comparison to the classical face verification problem, face retrieval poses additional challenges originating from database size. First, a different characteristic of recognition performance is required because retrieval scenarios have only very few correct face samples embedded in a large amount of imposters. In addition, the large number of samples in the database requires fast matching techniques. In this contribution, we present a face retrieval system which addresses these challenges. The first step consists of a set of measures to reduce frame descriptor dimension which saves processing time while keeping recognition performance. Afterwards, a novel Pyramid Mean Representation (PMR) of face videos is presented which allows for fast and accurate queries on large databases. The key concept is a hierarchical data representation with increasing sparsity which is used for an iterative query evaluation in a coarse to fine manner. The effectiveness of the proposed system is evaluated on the currently largest and most challenging public dataset of unconstrained videos, the YouTube Faces Database. In addition to the official verification test protocol, we define a protocol for face retrieval using a leave-one-out strategy. The proposed system achieves the best performance in this protocol with less processing time than baseline methods.

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Author information

Authors and Affiliations

  1. Vision and Fusion Lab, Karlsruhe Institute of Technology KIT, Karlsruhe, Germany

    Christian Herrmann & Jürgen Beyerer

  2. Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, Germany

    Christian Herrmann & Jürgen Beyerer

Authors
  1. Christian Herrmann
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  2. Jürgen Beyerer
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada at Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The University of Texas at Dallas, 75080, Richardson, TX, USA

    Ryan McMahan

  6. NextGen Interactions, 27604, Raleigh, NC, USA

    Jason Jerald

  7. Indiana University, 46202, Indianapolis, IN, USA

    Hui Zhang

  8. Microsoft Research, 1 Microsoft Way, 98052, Redmond, WA, USA

    Steven M. Drucker

  9. University of Delaware, 19716-2712, Newark, DE, USA

    Chandra Kambhamettu

  10. Intel Corp., 95054, Sata Clara, CA, USA

    Maha El Choubassi

  11. Computer Graphics and Interactive Media Lab, Department of Computer Science, University of Houston, 77004, Houston, TX, USA

    Zhigang Deng

  12. NVIDIA, 34788, Leesburg, FL, USA

    Mark Carlson

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© 2014 Springer International Publishing Switzerland

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Herrmann, C., Beyerer, J. (2014). Pyramid Mean Representation of Image Sequences for Fast Face Retrieval in Unconstrained Video Data. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_29

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  • DOI: https://doi.org/10.1007/978-3-319-14364-4_29

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14363-7

  • Online ISBN: 978-3-319-14364-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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