Person re-identification aims to identify whether pairs of images belong to the same person or not.
Typical methods for supervised sequence modeling are built upon the recurrent neural networks to capture temporal dependencies.
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 #3 on Face Verification on IJB-C (TAR @ FAR=0.01 metric)
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