Resources for biometric experiments

bob.bio.base provides open-source tools to run comparable and reproducible biometric recognition experiments. It covers the following biometrics traits:

Get Started

This package defines the structure of biometric experiments. After installing the necessary environment, you can try out a simple comparison between two (or more) samples using a face recognition algorithm from bob.bio.face, for example. Run the following command:

$ bob bio compare-samples --pipeline gabor_graph me.png not_me.png

The -p option indicates which algorithm should be used to compare the pictures. You can list all the available algorithms with:

$ resources.py --type pipeline

Todo

The name of the resource.py command should change.

Of course, with that command, you can run every possible biometric experiment by headbutting the problem and executing everything by hand. Or you could use the tools that we offer here to set up an experimentation pipeline, structure your data within a database interface and run a whole experiment in one swoop.

Citing our Publications

If you run biometric recognition experiments using the bob.bio framework, please cite at least one of the following in your scientific publication:

@inbook{guenther2016face,
  chapter = {Face Recognition in Challenging Environments: An Experimental and Reproducible Research Survey},
  author = {G\"unther, Manuel and El Shafey, Laurent and Marcel, S\'ebastien},
  editor = {Bourlai, Thirimachos},
  title = {Face Recognition Across the Imaging Spectrum},
  edition = {1},
  year = {2016},
  month = feb,
  publisher = {Springer}
}

@inproceedings{guenther2012facereclib,
  title = {An Open Source Framework for Standardized Comparisons of Face Recognition Algorithms},
  author = {G\"unther, Manuel and Wallace, Roy and Marcel, S\'ebastien},
  editor = {Fusiello, Andrea and Murino, Vittorio and Cucchiara, Rita},
  booktitle = {European Conference on Computer Vision (ECCV) Workshops and Demonstrations},
  series = {Lecture Notes in Computer Science},
  volume = {7585},
  year = {2012},
  month = oct,
  pages = {547-556},
  publisher = {Springer},
}

Users Guide

Reference Manual

References

TP91

M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71-86, 1991.

ZKC+98

W. Zhao, A. Krishnaswamy, R. Chellappa, D. Swets and J. Weng. Discriminant analysis of principal components for face recognition, pages 73-85. Springer Verlag Berlin, 1998.

Indices and tables