Face Recognition
556 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
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.
If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces
Finally, we assessed the effectiveness of data augmentation approaches on synthetic and authentic data, with the same goal in mind.
Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition
In this study, we apply the information-theoretic Privacy Funnel (PF) model to the domain of face recognition, developing a novel method for privacy-preserving representation learning within an end-to-end training framework.
Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches
The vulnerability of deep neural networks to adversarial patches has motivated numerous defense strategies for boosting model robustness.
A Parallel Attention Network for Cattle Face Recognition
Thus, we create the first large-scale cattle face recognition dataset, ICRWE, for wild environments.
