Face Identification
44 papers with code • 5 benchmarks • 6 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
Most implemented papers
A Supervised Learning Methodology for Real-Time Disguised Face Recognition in the Wild
The modern day scenario, where security is of prime concern, regular face identification techniques do not perform as required when the faces are disguised, which calls for a different approach to handle situations where intruders have their faces masked.
FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning
Combined variations containing low-resolution and occlusion often present in face images in the wild, e. g., under the scenario of video surveillance.
MarginDistillation: distillation for margin-based softmax
david-svitov/margindistillation
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The usage of convolutional neural networks (CNNs) in conjunction with a margin-based softmax approach demonstrates a state-of-the-art performance for the face recognition problem.
SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition
raphaelmemmesheimer/sl-dml
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Further, we show that our approach generalizes well in experiments on the UTD-MHAD dataset for inertial, skeleton and fused data and the Simitate dataset for motion capturing data.
Key-Nets: Optical Transformation Convolutional Networks for Privacy Preserving Vision Sensors
visym/keynet
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Modern cameras are not designed with computer vision or machine learning as the target application.
BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition
kakaoenterprise/BroadFace
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ECCV 2020
Moreover, we propose a novel compensation method to increase the number of referenced instances in the training stage.
Improvising the Learning of Neural Networks on Hyperspherical Manifold
barulalithb/stereo-angular-margin
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First, the stereographic projection is implied to transform data from Euclidean space ($\mathbb{R}^{n}$) to hyperspherical manifold ($\mathbb{S}^{n}$) to analyze the performance of angular margin losses.
Measuring Hidden Bias within Face Recognition via Racial Phenotypes
seymayucer/facialphenotypes
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We use the set of observable characteristics of an individual face where a race-related facial phenotype is hence specific to the human face and correlated to the racial profile of the subject.
DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Distance Improves Out-Of-Distribution Face Identification
anguyen8/deepface-emd
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CVPR 2022
Face identification (FI) is ubiquitous and drives many high-stake decisions made by law enforcement.
Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation
thetna/medical_image_segmentation
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We employed our method on two powerful and widely used semantic segmentation networks: UNet and U2Net to train in a multi-task setup.
