Biometric Scores 2014 (BIOSCOTE 2014)

<p><strong>Description</strong><br> <br> This dataset contains raw scores in plain text format of several biometric (face and speaker) recognition systems applied on several datasets such as BANCA, Arface, FRGC, GBU, LFW, Multi-PIE, MOBIO, CAS-PEAL, NIST SRE 2012.</p> <p>The biometric recognition systems are described in the aforementioned manuscript and encompasses Gaussian mixture models, inter-session variability modelling, joint factor analysis and probabilistic linear discriminant analysis.</p> <p>The databases considered are the following ones:</p> <ul> <li><a href="http://www.ee.surrey.ac.uk/CVSSP/banca/">BANCA</a></li> <li><a href="http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html">AR face database</a></li> <li><a href="http://www.nist.gov/itl/iad/ig/frgc.cfm">Face Recognition Grand Challenge version 2</a></li> <li><a href="http://www.nist.gov/itl/iad/ig/focs.cfm">The Good, The Bad and the Ugly</a></li> <li><a href="http://vis-www.cs.umass.edu/lfw">Labeled Faces in the Wild</a></li> <li><a href="http://www.multipie.org">Multi-PIE</a></li> <li><a href="https://www.idiap.ch/dataset/mobio">MOBIO</a></li> <li><a href="http://www.jdl.ac.cn/peal/index.html">CAS-PEAL</a></li> <li><a href="http://www.nist.gov/itl/iad/mig/sre12.cfm">NIST Speaker Recognition Evaluation 2012</a></li> </ul> <p>These scores allow to replicate easily and quickly the plots of the manuscript by using the following package:<br> http://pypi.python.org/pypi/xbob.thesis.elshafey2014</p> <p><br> <strong>Citation</strong></p> <p>If you use this dataset in your publication, we would appreciate that you cite the following thesis:</p> <p>Laurent El Shafey, &ldquo;Scalable Probabilistic Models for Face and Speaker Recognition&rdquo;, PhD thesis, 2014.<br> http://publications.idiap.ch/index.php/publications/show/2830</p>