5.6.4. The Labeled Faces in the Wild face recognition dataset¶
This dataset is a collection of JPEG pictures of famous people collected over the internet, all details are available on the official website:
Each picture is centered on a single face. The typical task is called Face Verification: given a pair of two pictures, a binary classifier must predict whether the two images are from the same person.
An alternative task, Face Recognition or Face Identification is: given the picture of the face of an unknown person, identify the name of the person by referring to a gallery of previously seen pictures of identified persons.
Both Face Verification and Face Recognition are tasks that are typically performed on the output of a model trained to perform Face Detection. The most popular model for Face Detection is called Viola-Jones and is implemented in the OpenCV library. The LFW faces were extracted by this face detector from various online websites.
scikit-learn provides two loaders that will automatically download,
cache, parse the metadata files, decode the jpeg and convert the
interesting slices into memmaped numpy arrays. This dataset size is more
than 200 MB. The first load typically takes more than a couple of minutes
to fully decode the relevant part of the JPEG files into numpy arrays. If
the dataset has been loaded once, the following times the loading times
less than 200ms by using a memmaped version memoized on the disk in the
~/scikit_learn_data/lfw_home/ folder using
The first loader is used for the Face Identification task: a multi-class classification task (hence supervised learning):
>>> from sklearn.datasets import fetch_lfw_people >>> lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4) >>> for name in lfw_people.target_names: ... print(name) ... Ariel Sharon Colin Powell Donald Rumsfeld George W Bush Gerhard Schroeder Hugo Chavez Tony Blair
The default slice is a rectangular shape around the face, removing most of the background:
>>> lfw_people.data.dtype dtype('float32') >>> lfw_people.data.shape (1288, 1850) >>> lfw_people.images.shape (1288, 50, 37)
Each of the
1140 faces is assigned to a single person id in the
>>> lfw_people.target.shape (1288,) >>> list(lfw_people.target[:10]) [5, 6, 3, 1, 0, 1, 3, 4, 3, 0]
The second loader is typically used for the face verification task: each sample is a pair of two picture belonging or not to the same person:
>>> from sklearn.datasets import fetch_lfw_pairs >>> lfw_pairs_train = fetch_lfw_pairs(subset='train') >>> list(lfw_pairs_train.target_names) ['Different persons', 'Same person'] >>> lfw_pairs_train.pairs.shape (2200, 2, 62, 47) >>> lfw_pairs_train.data.shape (2200, 5828) >>> lfw_pairs_train.target.shape (2200,)
Both for the
sklearn.datasets.fetch_lfw_pairs function it is
possible to get an additional dimension with the RGB color channels by
color=True, in that case the shape will be
(2200, 2, 62, 47, 3).
sklearn.datasets.fetch_lfw_pairs datasets is subdivided into
3 subsets: the development
train set, the development
test set and
10_folds set meant to compute performance metrics using a
10-folds cross validation scheme.
- Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. University of Massachusetts, Amherst, Technical Report 07-49, October, 2007.