Face Recognition
283 papers with code • 10 benchmarks • 35 datasets
Facial recognition is the task of making a positive identification of a face in a photo or video image against a pre-existing database of faces. It begins with detection - distinguishing human faces from other objects in the image - and then works on identification of those detected faces.
The state of the art tables for this task are contained mainly in the consistent parts of the task : the face verification and face identification tasks.
( Image credit: Face Verification )
Benchmarks
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Datasets
LFW
CASIA-WebFace
Extended Yale B
MS-Celeb-1M
Adience
VGG Face
UMDFaces
Partial-REID
RFW
Greatest papers with code
OpenFace: A general-purpose face recognition library with mobile applications
Cameras are becoming ubiquitous in the Internet of Things (IoT) and can use face recognition technology to improve context.
Ranked #26 on
Face Verification
on Labeled Faces in the Wild
Longitudinal Study of Child Face Recognition
Face comparison scores are obtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source matcher (FaceNet), and (iii) a simple sum fusion of scores obtained from COTS-A and FaceNet matchers.
FaceNet: A Unified Embedding for Face Recognition and Clustering
davidsandberg/facenet
•
•
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
Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces
deepinsight/insightface
•
•
ECCV 2020
In this paper, we relax the intra-class constraint of ArcFace to improve the robustness to label noise.
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
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
Face Analysis Project on MXNet
Ranked #1 on
Face Alignment
on IBUG
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
deepinsight/insightface
•
•
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
VGGFace2: A dataset for recognising faces across pose and age
The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimize the label noise.
Ranked #3 on
Face Verification
on IJB-C
(TAR @ FAR=0.01 metric)
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base.
Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
In this paper, we propose Fawkes, a system that helps individuals inoculate their images against unauthorized facial recognition models.