LFW: Labeled Faces in the Wild

LFW: Labeled Faces in the Wild:

This dataset contains labeled face images collected from the web with names of the people in the images as the labels. Some of these people have two or more number of images in the dataset. This dataset is designed for studying the problem of unconstrained face recognition and face verification. The original LFW dataset is available for download along with 3 sets of aligned images (funneled images, LFW-a, deep funneled).

Here is some information regarding this dataset:

• Number of images in the dataset: 13,000 images (10-fold cross validation is recommended and training and test splits can be downloaded from the dataset page)

• Number of identities: 5749

• Image resolution: 250×250

More details and links for download can be found on the dataset page http://vis-www.cs.umass.edu/lfw/.

If you use the any of these versions of the LFW image dataset:

Please make sure to cite the paper:

G. B. Huang, M. Ramesh, T. Berg, E. Learned-Miller, Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. University of Massachusetts, Amherst, Technical Report 07-49, October 2007.

If you use the LFW imaged aligned by deep funneling:

Please make sure to cite the paper:

G. B. Huang, M. Matter, H. Lee, E. Learned-Miller, Learning to Align from Scratch. Advances in Neural Information Processing Systems (NIPS), 2012.

If you use the LFW imaged aligned by funneling:

Please make sure to cite the paper:

G. B. Huang, V. Jain, E. Learned-Miller, Unsupervised Joint Alignment of Complex Images. International Conference on Computer Vision (ICCV), 2007.

keywords: Vision, Image, Face, Object Detection, Segmentation, In the Wild