Overview

This project implements a face recognition framework for Python (and MATLAB/GNU Octave) with:

• Preprocessing

  • Histogram Equalization
  • Local Binary Patterns
  • TanTriggsPreprocessing [TT2010]

• Feature Extraction

  • Eigenfaces [TP1991]

  • Fisherfaces [BHK1997]

  • Local Binary Patterns Histograms [AHP2004]

    • Original LBP
    • Extended LBP

  • Local Phase Quantization [HO2008]

• Classifier

  • k-Nearest Neighbor; available distance metrics

    • Euclidean Distance
    • Cosine Distance
    • ChiSquare Distance
    • Bin Ratio Distance

  • Support Vector Machines; using libsvm bindings. [Vapnik1998]

• Cross Validation

  • k-fold Cross Validation
  • Leave-One-Out Cross Validation
  • Leave-One-Class-Out Cross Validation

References

[TT2010]

Tan, X., and Triggs, B. “Enhanced local texture feature sets for face recognition under difficult lighting conditions.”. IEEE Transactions on Image Processing 19 (2010), 1635?650.

[TP1991]

Turk, M., and Pentland, A. “Eigenfaces for recognition.”. Journal of Cognitive Neuroscience 3 (1991), 71?86.

[BHK1997]

Belhumeur, P. N., Hespanha, J., and Kriegman, D. “Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection.”. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997), 711?720.

[AHP2004]

Ahonen, T., Hadid, A., and Pietikainen, M. “Face Recognition with Local Binary Patterns.”. Computer Vision - ECCV 2004 (2004), 469?481.

[HO2008]

Ojansivu V & Heikkil? J. “Blur insensitive texture classification using local phase quantization.” Proc. Image and Signal Processing (ICISP 2008), 5099:236-243.

[Vapnik1998]

Vapnik, V. “Statistical Learning Theory.”. John Wiley and Sons, New York, 1998.