Face Recognition
557 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
MeanCut: A Greedy-Optimized Graph Clustering via Path-based Similarity and Degree Descent Criterion
As the most typical graph clustering method, spectral clustering is popular and attractive due to the remarkable performance, easy implementation, and strong adaptability.
CLIPC8: Face liveness detection algorithm based on image-text pairs and contrastive learning
To tackle this issue, we propose a face liveness detection method based on image-text pairs and contrastive learning, dividing liveness attack problems in the financial field into eight categories and using text information to describe the images of these eight types of attacks.
Multi-Task Faces (MTF) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification Tasks
To address these issues, we present the Multi-Task Faces (MTF) image data set, a meticulously curated collection of face images designed for various classification tasks, including face recognition, as well as race, gender, and age classification.
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
ndido98/frcsyn
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Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail.
Masked Face Dataset Generation and Masked Face Recognition
In the post-pandemic era, wearing face masks has posed great challenge to the ordinary face recognition.
Loss Balancing for Fair Supervised Learning
khalilimahdi/loss_balancing_icml2023
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Imposing EL on the learning process leads to a non-convex optimization problem even if the loss function is convex, and the existing fair learning algorithms cannot properly be adopted to find the fair predictor under the EL constraint.
Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss
rafaelvareto/maximal-entropy-loss
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MEL modifies the traditional Cross-Entropy loss in favor of increasing the entropy for negative samples and attaches a penalty to known target classes in pursuance of gallery specialization.
Invariant Feature Regularization for Fair Face Recognition
panasonicconnect/invreg
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ICCV 2023
Unfortunately, face datasets inevitably capture the imbalanced demographic attributes that are ubiquitous in real-world observations, and the model learns biased feature that generalizes poorly in the minority group.
Learning Unified Representations for Multi-Resolution Face Recognition
As per the input, a resolution-specific BNet is used and the output are implanted as feature maps in the feature pyramid of TNet, at a layer with the same resolution.
Pairwise Similarity Learning is SimPLE
ydwen/opensphere
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ICCV 2023
In this paper, we focus on a general yet important learning problem, pairwise similarity learning (PSL).
