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In this page, we provide a list of Reproducible Research publications and other examples that build on top of Bob.
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If you want your publication to be listed here, please [contact us](https://groups.google.com/forum/?fromgroups#!forum/bob-devel).
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# Active Publications (using Bob v2)
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These publications use the new Bob framework and should work out-of-the-box.
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Please, [let us know](https://groups.google.com/forum/?fromgroups#!forum/bob-devel) in case you have trouble installing or executing any of the papers below.
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## Face Recognition in Challenging Environments: A Reproducible Research Survey
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We compare a list of face recognition algorithms' performances on different aspects of face recognition, such as facial expression, occlusion, pose and the availability of video data.
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* [Publication](http://publications.idiap.ch/index.php/publications/show/3313)
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* [Source code package](https://pypi.python.org/pypi/bob.chapter.FRICE)
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* [Documentation](http://pythonhosted.org/bob.chapter.FRICE/index.html)
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## Gender Classification by LUT based boosting of Overlapping Block Patterns
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We perform gender classification using a boosted strong classifier build out of weak look-up-table based classifiers, which are build from overlapping multi-block LBP codes.
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* [Publication](http://publications.idiap.ch/index.php/publications/show/3112)
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* [Source code package](https://pypi.python.org/pypi/bob.paper.SCIA2015)
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## Impact of Eye Detection Error on Face Recognition Performance
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We test, how several face recognition algorithms perform, when the images are mis-aligned due to an imprecise eye localization.
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* [Publication](http://publications.idiap.ch/index.php/publications/show/2981)
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* [Source code package](https://pypi.python.org/pypi/xfacereclib.paper.IET2015)
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# Older Publications (relying on Bob v1)
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These publications rely on an older version of Bob, which need [Bob v1 to be installed](Old Releases).
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## Score Calibration in Face Recognition
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We perform score calibration, a technique adopted from the speaker recognition domain, to perform score calibration using face recognition scores.
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* [Publication](http://publications.idiap.ch/index.php/publications/show/2822)
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* [Source code package](https://pypi.python.org/pypi/xfacereclib.paper.IET2014)
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## On the Improvements of Uni-modal and Bi-modal Fusions of Speaker and Face Recognition for Mobile Biometrics
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We use score calibration techniques to fuse the recognition scores from several speaker and face recognition algorithms and show that the fusion outperforms each single algorithm by far.
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* [Publication](http://publications.idiap.ch/index.php/publications/show/2688)
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* [Source code package](https://pypi.python.org/pypi/xbob.paper.BTFS2013)
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## An Open Source Framework for Standardized Comparisons of Face Recognition Algorithms
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We introduce the FaceRecLib (which in Bob v2 is split into the bob.bio packages), a tool to provide a fair comparison of face recognition algorithms.
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* [Publication](http://publications.idiap.ch/index.php/publications/show/2431)
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* [Source code package](https://pypi.python.org/pypi/xfacereclib.paper.BeFIT2012) |