Face identification is the task of matching a given face image to one in an existing database of faces. It is the second part of face recognition (the first part being detection). It is a one-to-many mapping: you have to find an unknown person in a database to find who that person is.
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99. 63%.
#3 best model for Face Verification on Labeled Faces in the Wild
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.
This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space.
#3 best model for Face Verification on MegaFace
This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels.
#2 best model for Age-Invariant Face Recognition on CAFR
Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
SOTA for Face Identification on IJB-B
However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces.
SOTA for Face Identification on IJB-A
To improve the discriminative and generalization ability of lightweight network for face recognition, we propose an efficient variable group convolutional network called VarGFaceNet.
SOTA for Face Recognition on AgeDB-30
We motivate and present Ring loss, a simple and elegant feature normalization approach for deep networks designed to augment standard loss functions such as Softmax.
#4 best model for Face Verification on Labeled Faces in the Wild
More specifically, we reformulate the softmax loss as a cosine loss by $L_2$ normalizing both features and weight vectors to remove radial variations, based on which a cosine margin term is introduced to further maximize the decision margin in the angular space.
#2 best model for Face Verification on Labeled Faces in the Wild