Face Identification
45 papers with code • 5 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 implementations
MS-Celeb-1M
MegaFace
IJB-A
IJB-B
DroneSURF
LTFT
PetFace
Most implemented papers
FaceNet: A Unified Embedding for Face Recognition and Clustering
serengil/deepface
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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
Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability.
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.
A Light CNN for Deep Face Representation with Noisy Labels
This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels.
Network In Network
With enhanced local modeling via the micro network, we are able to utilize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers.
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.
DeepID3: Face Recognition with Very Deep Neural Networks
Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity.
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.
GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations
HamadYA/GhostFaceNets
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IEEE Access 2023
The development of deep learning-based biometric models that can be deployed on devices with constrained memory and computational resources has proven to be a significant challenge.
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.
