.. _example_applications_plot_face_recognition.py: =================================================== Faces recognition example using eigenfaces and SVMs =================================================== The dataset used in this example is a preprocessed excerpt of the "Labeled Faces in the Wild", aka LFW_: http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB) .. _LFW: http://vis-www.cs.umass.edu/lfw/ Expected results for the top 5 most represented people in the dataset:: precision recall f1-score support Gerhard_Schroeder 0.91 0.75 0.82 28 Donald_Rumsfeld 0.84 0.82 0.83 33 Tony_Blair 0.65 0.82 0.73 34 Colin_Powell 0.78 0.88 0.83 58 George_W_Bush 0.93 0.86 0.90 129 avg / total 0.86 0.84 0.85 282 .. rst-class:: horizontal * .. image:: images/plot_face_recognition_1.png :scale: 47 * .. image:: images/plot_face_recognition_2.png :scale: 47 **Script output**:: Total dataset size: n_samples: 1288 n_features: 1850 n_classes: 7 Extracting the top 150 eigenfaces from 966 faces done in 0.957s Projecting the input data on the eigenfaces orthonormal basis done in 0.102s Fitting the classifier to the training set done in 24.331s Best estimator found by grid search: SVC(C=1000.0, cache_size=200, class_weight=auto, coef0=0.0, degree=3, gamma=0.005, kernel=rbf, probability=False, scale_C=True, shrinking=True, tol=0.001) Predicting the people names on the testing set done in 0.075s precision recall f1-score support Ariel Sharon 0.94 0.62 0.75 24 Colin Powell 0.69 0.92 0.79 49 Donald Rumsfeld 0.83 0.83 0.83 30 George W Bush 0.92 0.90 0.91 144 Gerhard Schroeder 0.74 0.85 0.79 20 Hugo Chavez 0.87 0.68 0.76 19 Tony Blair 0.82 0.75 0.78 36 avg / total 0.85 0.84 0.84 322 [[ 15 3 1 2 1 0 2] [ 0 45 1 1 0 0 2] [ 0 1 25 2 1 1 0] [ 1 9 2 129 1 1 1] [ 0 1 0 1 17 0 1] [ 0 2 0 1 3 13 0] [ 0 4 1 4 0 0 27]] **Python source code:** :download:`plot_face_recognition.py ` .. literalinclude:: plot_face_recognition.py :lines: 28-