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Face Recognition in Challenging Environments: An Experimental and Reproducible Research Survey

Abstract

One important type of biometric authentication is face recognition , a research area of high popularity with a wide spectrum of approaches that have been proposed in the last few decades. The majority of existing approaches are conceived for or evaluated on constrained still images. However, more recently research interests have shifted toward unconstrained “in-the-wild ” still images and videos. To some extent, current state-of-the-art systems are able to cope with variability due to pose, illumination, expression, and size, which represent the challenges in unconstrained face recognition. To date, only few attempts have addressed the problem of face recognition in mobile environment , where high degradation is present during both data acquisition and transmission. This book chapter deals with face recognition in mobile and other challenging environments, where both still images and video sequences are examined. We provide an experimental study of one commercial off-the-shelf (COTS) and four recent open-source face recognition algorithms , including color-based linear discriminant analysis (LDA) , local Gabor binary pattern histogram sequences (LGBPHSs) , Gabor grid graphs , and intersession variability (ISV) modeling . Experiments are performed on several freely available challenging still image and video face databases, including one mobile database, always following the evaluation protocols that are attached to the databases. Finally, we supply an easily extensible open-source toolbox to rerun all the experiments, which includes the modeling techniques, the evaluation protocols, and the metrics used in the experiments and provides a detailed description on how to regenerate the results.

Keywords

  • Facial Expression
  • Face Recognition
  • Gaussian Mixture Model
  • Recognition Algorithm
  • Gabor Wavelet

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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Fig. 11.1
Fig. 11.2
Fig. 11.3
Fig. 11.4
Fig. 11.5
Fig. 11.6
Fig. 11.7
Fig. 11.8
Fig. 11.9

Notes

  1. 1.

    http://pypi.python.org/pypi/xfacereclib.book.FRaES2016.

  2. 2.

    http://www.idiap.ch/software/bob.

  3. 3.

    http://www.cs.colostate.edu/facerec/algorithms/baselines2011.php.

  4. 4.

    http://pypi.python.org/pypi/facereclib.

  5. 5.

    http://www.reproducibleresearch.net.

  6. 6.

    For example, the results on LFW [16] are published under: http://vis-www.cs.umass.edu/lfw/results.html.

  7. 7.

    One example for reproducible research based on the FaceRecLib can be found under: http://pypi.python.org/pypi/xfacereclib.paper.BeFIT2012.

  8. 8.

    http://www.beat-eu.org/platform.

  9. 9.

    To avoid misunderstandings, we do not use the name CohortLDA as in [22], but we stick to the old name of the algorithm (LDA-IR).

  10. 10.

    The COTS vendor requested to stay anonymous.

  11. 11.

    http://github.com/idiap/bob/wiki/Packages.

  12. 12.

    The website http://www2.ece.ohio-state.edu/˜aleix/ARdatabase.html re-ports more than 4000 images, but we could not reach the controller of the database to clarify the difference.

  13. 13.

    http://lear.inrialpes.fr/people/guillaumin/data.php.

  14. 14.

    To be comparable to the occlusion and both protocols, the same training set, i.e., including occluded faces, was also used in the illumination protocol.

  15. 15.

    We just use the pickle module of Python to store the LDA-IR data. Table 11.2(b) shows the resulting file size on disk.

  16. 16.

    http://www.idiap.ch/resource/biometric.

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Acknowledgements

This evaluation has received funding from the European Community’s FP7 under grant agreements 238803 (BBfor2: bbfor2.net ) and 284989 (BEAT: beat-eu.org ). This work is based on open-source software provided by the Idiap Research Institute and the Colorado State University. The authors want to thank all contributors of the software for their great work.

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Günther, M., Shafey, L.E., Marcel, S. (2016). Face Recognition in Challenging Environments: An Experimental and Reproducible Research Survey. In: Bourlai, T. (eds) Face Recognition Across the Imaging Spectrum. Springer, Cham. https://doi.org/10.1007/978-3-319-28501-6_11

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