82 papers with code • 19 benchmarks • 18 datasets
Face verification is the task of comparing a candidate face to another, and verifying whether it is a match. It is a one-to-one mapping: you have to check if this person is the correct one.
( Image credit: Pose-Robust Face Recognition via Deep Residual Equivariant Mapping )
Cameras are becoming ubiquitous in the Internet of Things (IoT) and can use face recognition technology to improve context.
Ranked #27 on Face Verification on Labeled Faces in the Wild
On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99. 63%.
Ranked #1 on Disguised Face Verification on MegaFace
The experiment demonstrates no loss of accuracy when training with only 10\% randomly sampled classes for the softmax-based loss functions, compared with training with full classes using state-of-the-art models on mainstream benchmarks.
Ranked #1 on Face Identification on MegaFace
Face Analysis Project on MXNet
Ranked #1 on Face Verification on 2019_test set
One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large-scale face recognition is the design of appropriate loss functions that enhance discriminative power.
Ranked #2 on Face Verification on Labeled Faces in the Wild
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)