24 code implementations • 23 Oct 2017 • Qiong Cao, Li Shen, Weidi Xie, Omkar M. Parkhi, Andrew Zisserman
The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimize the label noise.
Ranked #1 on Face Verification on IJB-C (training dataset metric)
no code implementations • 12 Mar 2016 • Nate Crosswhite, Jeffrey Byrne, Omkar M. Parkhi, Chris Stauffer, Qiong Cao, Andrew Zisserman
Face recognition performance evaluation has traditionally focused on one-to-one verification, popularized by the Labeled Faces in the Wild dataset for imagery and the YouTubeFaces dataset for videos.
Ranked #8 on Face Verification on IJB-A
no code implementations • CVPR 2014 • Omkar M. Parkhi, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
Our goal is to learn a compact, discriminative vector representation of a face track, suitable for the face recognition tasks of verification and classification.