VGG Face Descriptor

http://www.robots.ox.ac.uk/~vgg/software/vgg_face/

VGG Face Descriptor

Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman

Overview

This page contains the download links for the source code for computing the VGG-Face CNN descriptor, described in [1].

The VGG-Face CNN descriptors are computed using our CNN implementation based on the VGG-Very-Deep-16 CNN architecture as described in [1] and are evaluated on the Labeled Faces in the Wild [2] and the YouTube Faces [3] dataset.

Additionally the code also contains our fast implementation of the DPM Face detector of [3] using the cascade DPM code of [4].

Results

 

Downloads

Filename	Description
vgg_face_matconvnet.tar.gz	Face detection and VGG Face descriptor source code and models (MatConvNet)
vgg_face_torch.tar.gz	VGG Face descriptor source code and models (Torch)
vgg_face_caffe.tar.gz	VGG Face descriptor source code and models (Caffe)

 

Relevant Publications

 

[1] O. M. Parkhi, A. Vedaldi, A. Zisserman

Deep Face Recognition  

British Machine Vision Conference, 2015

Bibtex | Abstract | PDF | Poster | Presentation | Models and Code | All

@InProceedings{Parkhi15,   author       = "Parkhi, O. M. and Vedaldi, A. and Zisserman, A.",   title        = "Deep Face Recognition",   booktitle    = "British Machine Vision Conference",   year         = "2015", } 

[2] G. B. Huang, M. Ramesh, T. Berg, E. Learned-Miller

Labeled faces in the wild: A database for studying face recognition in unconstrained environments.

Technical Report 07-49, University of Massachusetts, Amherst, 2007.

[3] L. Wolf, T. Hassner, I. Maoz

Face Recognition in Unconstrained Videos with Matched Background Similarity.

Computer Vision and Pattern Recognition (CVPR), 2011.

[4] M. Mathias, R. Benenson, M. Pedersoli, L. Van Gool

Face detection without bells and whistles.

European Conference on Computer Vision, 2014.

[5] P. Felzenszwalb, R. Girshick, D. McAllester

Cascade Object Detection with Deformable Part Models.

Computer Vision and Pattern Recognition (CVPR), 2010.