1 research outputs found
Surveillance Face Recognition Challenge
Face recognition (FR) is one of the most extensively investigated problems in
computer vision. Significant progress in FR has been made due to the recent
introduction of the larger scale FR challenges, particularly with constrained
social media web images, e.g. high-resolution photos of celebrity faces taken
by professional photo-journalists. However, the more challenging FR in
unconstrained and low-resolution surveillance images remains largely
under-studied. To facilitate more studies on developing FR models that are
effective and robust for low-resolution surveillance facial images, we
introduce a new Surveillance Face Recognition Challenge, which we call the
QMUL-SurvFace benchmark. This new benchmark is the largest and more importantly
the only true surveillance FR benchmark to our best knowledge, where
low-resolution images are not synthesised by artificial down-sampling of native
high-resolution images. This challenge contains 463,507 face images of 15,573
distinct identities captured in real-world uncooperative surveillance scenes
over wide space and time. As a consequence, it presents an extremely
challenging FR benchmark. We benchmark the FR performance on this challenge
using five representative deep learning face recognition models, in comparison
to existing benchmarks. We show that the current state of the arts are still
far from being satisfactory to tackle the under-investigated surveillance FR
problem in practical forensic scenarios. Face recognition is generally more
difficult in an open-set setting which is typical for surveillance scenarios,
owing to a large number of non-target people (distractors) appearing open
spaced scenes. This is evidently so that on the new Surveillance FR Challenge,
the top-performing CentreFace deep learning FR model on the MegaFace benchmark
can now only achieve 13.2% success rate (at Rank-20) at a 10% false alarm rate.Comment: The QMUL-SurvFace challenge is publicly available at
https://qmul-survface.github.io