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.
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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.
SOTA for Face Identification on MegaFace
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.
#4 best model for Face Identification 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
Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity.
#8 best model for Face Verification on Labeled Faces in the Wild
To improve the discriminative and generalization ability of lightweight network for face recognition, we propose an efficient variable group convolutional network called VarGFaceNet.
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.
#3 best model for Face Verification on YouTube Faces DB
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.
#9 best model for Face Verification on Labeled Faces in the Wild