Face Recognition Algorithms and Databases

This package is part of the bob.bio packages, which provide open source tools to run comparable and reproducible biometric recognition experiments. In this package, tools for executing face recognition experiments are provided. This includes:

• Preprocessors to detect, align and photometrically enhance face images
• Feature extractors that extract features from facial images
• Recognition algorithms that are specialized on facial features, and
• Facial image databases including their protocols.

Additionally, a set of baseline algorithms are defined, which integrate well with the two other bob.bio packages:

• bob.bio.gmm defines algorithms based on Gaussian mixture models
• bob.bio.csu provides wrapper classes of the CSU Face Recognition Resources (only Python 2.7 compatible)

For more detailed information about the structure of the bob.bio packages, please refer to the documentation of bob.bio.base. Particularly, the installation of this and other bob.bio packages, please read the Installation Instructions.

In the following, we provide more detailed information about the particularities of this package only.

Users Guide

• Executing Baseline Algorithms
  • Setting up your Database
  • Running Baseline Experiments
  • The Algorithms
  • Baseline Results
• Implementation Details
  • Image preprocessing
  • Registered Resources

Reference Manual

• Tools implemented in bob.bio.face
  • Summary
  • Preprocessors
  • Extractors
  • Algorithms

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.

[MWP98]

B. Moghaddam, W. Wahid and A. Pentland. Beyond eigenfaces: probabilistic matching for face recognition. IEEE International Conference on Automatic Face and Gesture Recognition, pages 30-35. 1998.

[PBD+11]

P.J. Phillips, J.R. Beveridge, B.A. Draper, G. Givens, A.J. O’Toole, D.S. Bolme, J. Dunlop, Y.M. Lui, H. Sahibzada and S. Weimer. An introduction to the good, the bad, & the ugly face recognition challenge problem. Automatic face gesture recognition and workshops (FG 2011), pages 346-353. 2011.

[LBP+12]

Y.M. Lui, D. Bolme, P.J. Phillips, J.R. Beveridge and B.A. Draper. Preliminary studies on the good, the bad, and the ugly face recognition challenge problem. Computer vision and pattern recognition workshops (CVPRW), pages 9-16. 2012.

[GHW12]

M. Günther, D. Haufe and R.P. Würtz. Face recognition with disparity corrected Gabor phase differences. In Artificial neural networks and machine learning, volume 7552 of Lecture Notes in Computer Science, pages 411-418. 9/2012.

[ZSG+05]

W. Zhang, S. Shan, W. Gao, X. Chen and H. Zhang. Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition. Computer Vision, IEEE International Conference on, 1:786-791, 2005.

[MM09]

C. McCool, S. Marcel. Parts-based face verification using local frequency bands. In Advances in biometrics, volume 5558 of Lecture Notes in Computer Science. 2009.

[WMM+12]

R. Wallace, M. McLaren, C. McCool and S. Marcel. Cross-pollination of normalisation techniques from speaker to face authentication using Gaussian mixture models. IEEE Transactions on Information Forensics and Security, 2012.

[WMM+11]

R. Wallace, M. McLaren, C. McCool and S. Marcel. Inter-session variability modelling and joint factor analysis for face authentication. International Joint Conference on Biometrics. 2011.

[Pri07]

S. J. D. Prince. Probabilistic linear discriminant analysis for inferences about identity. Proceedings of the International Conference on Computer Vision. 2007.

[ESM+13]

L. El Shafey, Chris McCool, Roy Wallace and Sébastien Marcel. A scalable formulation of probabilistic linear discriminant analysis: applied to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7):1788-1794, 7/2013.

[WM12]

R. Wallace and M. McLaren. Total variability modelling for face verification. IET Biometrics, vol.1, no.4, 188-199, 12/2012

[TT10]

X. Tan and B. Triggs. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, 19(6):1635-1650, 2010.

[WLW04]

H. Wang, S.Z. Li and Y. Wang. Face recognition under varying lighting conditions using self quotient image. In IEEE International Conference on Automatic Face and Gesture Recognition (AFGR), pages 819-824. 2004.

[HRM06]

G. Heusch, Y. Rodriguez, and S. Marcel. Local Binary Patterns as an Image Preprocessing for Face Authentication. In IEEE International Conference on Automatic Face and Gesture Recognition (AFGR), 2006.

[WFK97]

L. Wiskott, J.-M. Fellous, N. Krüger and C.v.d. Malsburg. Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:775-779, 1997.

[ZSQ+09]

W. Zhang, S. Shan, L. Qing, X. Chen and W. Gao. Are Gabor phases really useless for face recognition? Pattern Analysis & Applications, 12:301-307, 2009.

[GW09]

M. Günther and R.P. Würtz. Face detection and recognition using maximum likelihood classifiers on Gabor graphs. International Journal of Pattern Recognition and Artificial Intelligence, 23(3):433-461, 2009.

[GWM12]

M. Günther, R. Wallace and S. Marcel. An Open Source Framework for Standardized Comparisons of Face Recognition Algorithms. Computer Vision - ECCV 2012. Workshops and Demonstrations, LNCS, 7585, 547-556, 2012.