Learning Scikit-learn: Machine Learning in Python

IPython Notebook for Chapter 2: Supervised Learning - Image Recognition with Support Vector Machines

_In this notebook, we show how to perform face recognition using Support Vector Machines. We will use the Olivetti faces dataset, included in Scikit-learn. More info at: http://scikit-learn.org/stable/datasets/olivetti_faces.html_

Start by importing numpy, scikit-learn, and pyplot, the Python libraries we will be using in this chapter. Show the versions we will be using (in case you have problems running the notebooks).

%pylab inline
import IPython
import sklearn as sk
import numpy as np
import matplotlib
import matplotlib.pyplot as plt

print 'IPython version:', IPython.__version__
print 'numpy version:', np.__version__
print 'scikit-learn version:', sk.__version__
print 'matplotlib version:', matplotlib.__version__

Populating the interactive namespace from numpy and matplotlib
IPython version: 2.1.0
numpy version: 1.8.2
scikit-learn version: 0.15.1
matplotlib version: 1.3.1

Import the olivetti faces dataset

from sklearn.datasets import fetch_olivetti_faces

# fetch the faces data
faces = fetch_olivetti_faces()

print faces.DESCR

Modified Olivetti faces dataset.

The original database was available from (now defunct)

    http://www.uk.research.att.com/facedatabase.html

The version retrieved here comes in MATLAB format from the personal
web page of Sam Roweis:

    http://www.cs.nyu.edu/~roweis/

There are ten different images of each of 40 distinct subjects. For some
subjects, the images were taken at different times, varying the lighting,
facial expressions (open / closed eyes, smiling / not smiling) and facial
details (glasses / no glasses). All the images were taken against a dark
homogeneous background with the subjects in an upright, frontal position (with
tolerance for some side movement).

The original dataset consisted of 92 x 112, while the Roweis version
consists of 64x64 images.

Let's look at the data, faces.images has 400 images of faces, each one is composed by a matrix of 64x64 pixels. faces.data has the same data but in rows of 4096 attributes instead of matrices (4096 = 64x64)

print faces.keys()
print faces.images.shape
print faces.data.shape
print faces.target.shape

['images', 'data', 'target', 'DESCR']
(400, 64, 64)
(400, 4096)
(400,)

We don't have to scale attributes, because data is already normalized

print np.max(faces.data)
print np.min(faces.data)
print np.mean(faces.data)

1.0
0.0
0.547046

Plot the first 20 images. We have 40 individuals with 10 different images each.

def print_faces(images, target, top_n):
    # set up the figure size in inches
    fig = plt.figure(figsize=(12, 12))
    fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
    for i in range(top_n):
        # plot the images in a matrix of 20x20
        p = fig.add_subplot(20, 20, i + 1, xticks=[], yticks=[])
        p.imshow(images[i], cmap=plt.cm.bone)
        
        # label the image with the target value
        p.text(0, 14, str(target[i]))
        p.text(0, 60, str(i))
    

print_faces(faces.images, faces.target, 20)

Plot all the faces in a matrix of 20x20, for each one, we'll put it target value in the top left corner and it index in the bottom left corner. It may take a few seconds.

print_faces(faces.images, faces.target, 400)