Face Identification
34 papers with code • 4 benchmarks • 7 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
These leaderboards are used to track progress in Face Identification
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Libraries
Use these libraries to find Face Identification models and implementationsDatasets
MS-Celeb-1M
MegaFace
IJB-A
IJB-B
LTFT
1,417 People – 3D Living_Face & Anti_Spoofing Data
64,378 Images Data of 1,073 Dogs' Noses
Most implemented papers
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%.
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.
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.
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.
A Light CNN for Deep Face Representation with Noisy Labels
AlfredXiangWu/face_verification_experiment
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This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels.
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.
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.
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.
Editable Neural Networks
editable-ICLR2020/editable
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ICLR 2020
We empirically demonstrate the effectiveness of this method on large-scale image classification and machine translation tasks.
GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition
SeungyounShin/GroupFace
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CVPR 2020
In the field of face recognition, a model learns to distinguish millions of face images with fewer dimensional embedding features, and such vast information may not be properly encoded in the conventional model with a single branch.
