Face Recognition
552 papers with code • 22 benchmarks • 61 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
These leaderboards are used to track progress in Face Recognition
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Libraries
Use these libraries to find Face Recognition models and implementationsDatasets
LFW
CASIA-WebFace
MS-Celeb-1M
Extended Yale B
MORPH
IJB-B
Adience
VGG Face
RFW
Partial-REID
Subtasks
Latest papers with no code
MHLR: Moving Haar Learning Rate Scheduler for Large-scale Face Recognition Training with One GPU
MHLR supports large-scale FR training with only one GPU, which is able to accelerate the model to 1/4 of its original training time without sacrificing more than 1% accuracy.
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Synthetic data is gaining increasing relevance for training machine learning models.
Adversarial Identity Injection for Semantic Face Image Synthesis
Among all the explored techniques, Semantic Image Synthesis (SIS) methods, whose goal is to generate an image conditioned on a semantic segmentation mask, are the most promising, even though preserving the perceived identity of the input subject is not their main concern.
FaceCat: Enhancing Face Recognition Security with a Unified Generative Model Framework
Motivated by the rich structural and detailed features of face generative models, we propose FaceCat which utilizes the face generative model as a pre-trained model to improve the performance of FAS and FAD.
FaceFilterSense: A Filter-Resistant Face Recognition and Facial Attribute Analysis Framework
To mitigate these limitations, we aim to perform a holistic impact analysis of the latest filters and propose an user recognition model with the filtered images.
Trashbusters: Deep Learning Approach for Litter Detection and Tracking
This research focuses on automating the penalization of litterbugs, addressing the persistent problem of littering in public places.
Unified Physical-Digital Attack Detection Challenge
Based on this dataset, we organized a Unified Physical-Digital Face Attack Detection Challenge to boost the research in Unified Attack Detections.
Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs
Morphing attacks are an emerging threat to state-of-the-art Face Recognition (FR) systems, which aim to create a single image that contains the biometric information of multiple identities.
The Impact of Print-and-Scan in Heterogeneous Morph Evaluation Scenarios
Face morphing attacks present an emerging threat to the face recognition system.
SDFR: Synthetic Data for Face Recognition Competition
The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data.
