Face Identification
41 papers with code • 4 benchmarks • 5 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Face Identification models and implementations
MS-Celeb-1M
MegaFace
IJB-A
IJB-B
LTFT
Most implemented papers
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
dooleys/fr-nas
•
•
NeurIPS 2023
Our search outputs a suite of models which Pareto-dominate all other high-performance architectures and existing bias mitigation methods in terms of accuracy and fairness, often by large margins, on the two most widely used datasets for face identification, CelebA and VGGFace2.
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition
To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract "Multi-Directional Multi-Level Dual-Cross Patterns" (MDML-DCPs) from face images.
Robust and Low-Rank Representation for Fast Face Identification with Occlusions
In this paper we propose an iterative method to address the face identification problem with block occlusions.
One-shot Face Recognition by Promoting Underrepresented Classes
First, we build a face feature extraction model, and improve its performance, especially for the persons with very limited training samples, by introducing a regularizer to the cross entropy loss for the multi-nomial logistic regression (MLR) learning.
Group-level Emotion Recognition using Transfer Learning from Face Identification
In this paper, we describe our algorithmic approach, which was used for submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017) group-level emotion recognition sub-challenge.
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.
Git Loss for Deep Face Recognition
kjanjua26/Git-Loss-For-Deep-Face-Recognition
•
•
Conventionally, CNNs have been trained with softmax as supervision signal to penalize the classification loss.
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
•
•
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.
