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Results

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Unsupervised Results

Image-Restricted Training Results

Unrestricted Training Results

Notes

Results in red indicate methods accepted but not yet published (e.g. accepted to an upcoming conference).

* Each point on the curve represents the average over the 10 folds of (false positive rate, true positive rate) for a fixed threshold.

(u) indicates ROC curve is for the unrestricted setting.

Generating ROC Curves

The following script can be used to generate ROC curves using gnuplot: create_lfw_all_roc.p (only restricted / unrestricted / unsupervised).

The script takes in one text file for each method, containing on each line a point on the ROC curve, i.e. average true positive rate, followed by average false positive rate, separated by a single space. Additional methods can be added to the script by adding on to the plot command, e.g.

plot "nowak-original-roc.txt" using 2:1 with lines title "Nowak, original", \
     "nowak-funneled-roc.txt" using 2:1 with lines title "Nowak, funneled", \
     "new-method-roc.txt" using 2:1 with lines title "New Method"

Existing ROC files can be downloaded here:

• eigenfaces-original-roc.txt
• nowak-original-roc.txt
• nowak-funneled-roc.txt
• merl-roc.txt
• combined-roc.txt (Merl+Nowak)
• combined16.txt (Hybrid descriptor-based)
• funneled-v1-like-roc.txt (Pixels/MKL)
• funneled-v1-like-roc.txt (V1-like/MKL)
• guillaumin-ldml.txt
• hybrid_with_sift_aligned.txt
• guillaumin-ldmlmknn.txt
• multishot_unrestricted_bmvc09.txt
• multishot_unrestricted_single_feature_bmvc09.txt
• accv09-wolf-hassner-taigman-roc.txt
• kumar_attrs.txt
• kumar_sims.txt
• kumar_attrs_sims.txt
• kumar_human_orig.txt
• kumar_human_crop.txt
• kumar_human_inv.txt
• lbp_eurasip2009_jrs-roc.txt
• gabor_eurasip2009_jrs-roc.txt
• sift_eurasip2009_jrs-roc.txt

Notes: gnuplot is multi-platform and freely distributed, and can be downloaded here. create_lfw_roc.p can either be run as a shell script on Unix/Linux machines (e.g. chmod u+x create_lfw_roc.p; ./create_lfw_roc.p) or loaded through gnuplot (e.g. at the gnuplot command line gnuplot> load "create_lfw_roc.p").

Methods

1. Matthew A. Turk and Alex P. Pentland.
   Face Recognition Using Eigenfaces.
   Computer Vision and Pattern Recognition (CVPR), 1991.
   [pdf]
   
   
2. Eric Nowak and Frederic Jurie.
   Learning visual similarity measures for comparing never seen objects.
   Computer Vision and Pattern Recognition (CVPR), 2007.
   [pdf]
   [webpage]
   
   Results were obtained using the binary available from the paper's webpage. View 1 of the database was used to compute the cut-off threshold used in computing mean classification accuracy on View 2. For each of the 10 folds of View 2 of the database, 9 of the sets were used as training, the similarity measures were computed for the held out test set, and the threshold value was used to classify pairs as matched or mismatched. This procedure was performed both on the original images as well as the set of aligned images from the funneled parallel database.
   
   We used the same parameters given on the paper's webpage, with C=1 for the SVM, specifically:
   
   pRazSimiERCF -verbose 2 -ntrees 5 -maxleavesnb 25000 -nppL 100000 -ncondtrial 1000 -nppT 1000 -wmin 15 -wmax 100 -neirelsize 1 -svmc 1
   
   
3. Gary B. Huang, Vidit Jain, and Erik Learned-Miller.
   Unsupervised joint alignment of complex images.
   International Conference on Computer Vision (ICCV), 2007.
   [pdf]
   [webpage]
   
   Face images were aligned using publicly available source code from project webpage.
   
   
4. Gary B. Huang, Michael J. Jones, and Erik Learned-Miller.
   LFW Results Using a Combined Nowak Plus MERL Recognizer.
   Faces in Real-Life Images Workshop in European Conference on Computer Vision (ECCV), 2008.
   [pdf]
   
   
5. Lior Wolf, Tal Hassner, and Yaniv Taigman.
   Descriptor Based Methods in the Wild.
   Faces in Real-Life Images Workshop in European Conference on Computer Vision (ECCV), 2008.
   [pdf]
   [webpage]
   
   
6. Conrad Sanderson and Brian C. Lovell.
   Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference.
   International Conference on Biometrics (ICB), 2009.
   [pdf]
   
   
7. Nicolas Pinto, James J. DiCarlo, and David D. Cox
   How far can you get with a modern face recognition test set using only simple features? Computer Vision and Pattern Recognition (CVPR), 2009.
   [pdf]
   
   
8. Matthieu Guillaumin, Jakob Verbeek, and Cordelia Schmid.
   Is that you? Metric Learning Approaches for Face Identification.
   International Conference on Computer Vision (ICCV), 2009.
   [pdf]
   [webpage]
   
   
9. Yaniv Taigman, Lior Wolf, and Tal Hassner.
   Multiple One-Shots for Utilizing Class Label Information.
   British Machine Vision Conference (BMVC), 2009.
   [pdf]
   [webpage]
   
   
10. Lior Wolf, Tal Hassner, and Yaniv Taigman.
    Similarity Scores based on Background Samples.
    Asian Conference on Computer Vision (ACCV), 2009.
    [pdf]
    
    
11. Neeraj Kumar, Alexander C. Berg, Peter N. Belhumeur, and Shree K. Nayar.
    Attribute and Simile Classifiers for Face Verification.
    International Conference on Computer Vision (ICCV), 2009.
    [pdf]
    [webpage]
    
    
12. Javier Ruiz-del-Solar, Rodrigo Verschae, and Mauricio Correa.
    Recognition of Faces in Unconstrained Environments: A Comparative Study.
    EURASIP Journal on Advances in Signal Processing (Recent Advances in Biometric Systems: A Signal Processing Perspective), Vol. 2009, Article ID 184617, 19 pages.
    [pdf]