Face Identification
29 papers with code • 4 benchmarks • 4 datasets
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.
Benchmarks
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MS-Celeb-1M
MegaFace
IJB-A
IJB-B
Greatest papers with code
FaceNet: A Unified Embedding for Face Recognition and Clustering
davidsandberg/facenet
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CVPR 2015
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
Partial FC: Training 10 Million Identities on a Single Machine
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
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
deepinsight/insightface
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CVPR 2019
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 MegaFace
CosFace: Large Margin Cosine Loss for Deep Face Recognition
PaddlePaddle/PaddleClas
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•
CVPR 2018
The central task of face recognition, including face verification and identification, involves face feature discrimination.
Ranked #3 on
Face Verification
on YouTube Faces DB
Deep Learning Face Representation by Joint Identification-Verification
serengil/deepface
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NeurIPS 2014
The learned DeepID2 features can be well generalized to new identities unseen in the training data.
Ranked #18 on
Face Verification
on Labeled Faces in the Wild
Deep Learning Face Representation from Predicting 10,000 Classes
When learned as classifiers to recognize about 10, 000 face identities in the training set and configured to keep reducing the neuron numbers along the feature extraction hierarchy, these deep ConvNets gradually form compact identity-related features in the top layers with only a small number of hidden neurons.
Ranked #24 on
Face Verification
on Labeled Faces in the Wild
SphereFace: Deep Hypersphere Embedding for Face Recognition
wy1iu/sphereface
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•
CVPR 2017
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.
Ranked #1 on
Face Verification
on CK+
A Light CNN for Deep Face Representation with Noisy Labels
AlfredXiangWu/face_verification_experiment
•
•
This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels.
Ranked #2 on
Age-Invariant Face Recognition
on CAFR
FacePoseNet: Making a Case for Landmark-Free Face Alignment
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.
Ranked #1 on
Facial Landmark Detection
on 300W
(Mean Error Rate metric)
Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
penincillin/DREAM
•
•
CVPR 2018
However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces.
Ranked #1 on
Face Identification
on IJB-A