SOTAVerified

Face Recognition

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 )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2251–2300 of 2329 papers

TitleStatusHype
Dynamic Region-Aware Convolution—0
Ear Recognition—0
ECOC-Based Training of Neural Networks for Face Recognition—0
Edge-GPU Based Face Tracking for Face Detection and Recognition Acceleration—0
Effective Adapter for Face Recognition in the Wild—0
Effective Face Frontalization in Unconstrained Images—0
Effect of Lossy Compression Algorithms on Face Image Quality and Recognition—0
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild—0
Efficient aggregation of face embeddings for decentralized face recognition deployments (extended version)—0
Efficient Decision-based Black-box Adversarial Attacks on Face Recognition—0
Efficient Estimation of Regularized Tyler's M-Estimator Using Approximate LOOCV—0
Efficient Expression Neutrality Estimation with Application to Face Recognition Utility Prediction—0
Efficient Face Alignment via Locality-constrained Representation for Robust Recognition—0
Efficient Facial Representations for Age, Gender and Identity Recognition in Organizing Photo Albums using Multi-output CNN—0
Efficient High-Dimensional Data Representation Learning via Semi-Stochastic Block Coordinate Descent Methods—0
Efficient Low-Resolution Face Recognition via Bridge Distillation—0
Efficient Masked Face Recognition Method during the COVID-19 Pandemic—0
EFHQ: Multi-purpose ExtremePose-Face-HQ dataset—0
Ef-QuantFace: Streamlined Face Recognition with Small Data and Low-Bit Precision—0
EGAIN: Extended GAn INversion—0
EigenHearts: Cardiac Diseases Classification Using EigenFaces Approach—0
Eight Years of Face Recognition Research: Reproducibility, Achievements and Open Issues—0
Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches—0
Emotion-Aware Human Attention Prediction—0
End-To-End Face Detection and Recognition—0
End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning—0
Enhanced Attacks on Defensively Distilled Deep Neural Networks—0
Enhanced Face Authentication With Separate Loss Functions—0
Enhanced Facial Recognition Framework based on Skin Tone and False Alarm Rejection—0
Enhanced Local Binary Patterns for Automatic Face Recognition—0
Enhancing Convolutional Neural Networks for Face Recognition with Occlusion Maps and Batch Triplet Loss—0
Enhancing Face Recognition With Self-Supervised 3D Reconstruction—0
Enhancing Generalization of Invisible Facial Privacy Cloak via Gradient Accumulation—0
Enhancing Mobile Privacy and Security: A Face Skin Patch-Based Anti-Spoofing Approach—0
Enhancing Privacy in Face Analytics Using Fully Homomorphic Encryption—0
Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation—0
E-Pro: Euler Angle and Probabilistic Model for Face Detection and Recognition—0
ErasableMask: A Robust and Erasable Privacy Protection Scheme against Black-box Face Recognition Models—0
Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination—0
Estimating Structural Disparities for Face Models—0
Evaluating Proposed Fairness Models for Face Recognition Algorithms—0
Evaluating the Effectiveness of Automated Identity Masking (AIM) Methods with Human Perception and a Deep Convolutional Neural Network (CNN)—0
Controllable Evaluation and Generation of Physical Adversarial Patch on Face Recognition—0
Evaluation of Momentum Diverse Input Iterative Fast Gradient Sign Method (M-DI2-FGSM) Based Attack Method on MCS 2018 Adversarial Attacks on Black Box Face Recognition System—0
Evaluation-oriented Knowledge Distillation for Deep Face Recognition—0
Evolutionary Cost-sensitive Extreme Learning Machine—0
Evolutionary latent space search for driving human portrait generation—0
Evolutionary Trigger Detection and Lightweight Model Repair Based Backdoor Defense—0
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition—0
Examplers based image fusion features for face recognition—0
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All datasetsLFWCFP-FPCASIA-WebFace+masksCelebA+masksMLFWAgeDB-30Color FERETIJB-BCALFWmebeblurfMFW+ (M-M)MFW+ (U-M)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PIC - ArcFaceFNMR [%] @ 10-3 FMR4.38—Unverified
2GhostFaceNetV2-1 (MS1MV3)Accuracy1—Unverified
3SymFace + AdaFace + ResNet100 +WebFace (MS1MV2)Accuracy1—Unverified
4ProdpolyAccuracy1—Unverified
5DiscFaceAccuracy1—Unverified
6ArcFace + MS1MV2 + R100Accuracy1—Unverified
7DCQAccuracy1—Unverified
8AdaFace + WebFace4M + R100Accuracy1—Unverified
9EdgeFace - S (g=0.5)Accuracy1—Unverified
10EdgeFace - XS (g=0.6)Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
1GhostFaceNetV2-1Accuracy0.99—Unverified
2SymFace + AdaFace + ResNet100 +WebFaceAccuracy0.99—Unverified
3ElasticFace-ArcAccuracy0.99—Unverified
4DiscFaceAccuracy0.99—Unverified
5CircleLoss(ours)Accuracy0.96—Unverified
6EdgeFace - S (g=0.5)Accuracy0.96—Unverified
7DCQAccuracy0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1Fine-tuned ArcFaceAccuracy91.47—Unverified
2Fine-tuned FaceNetAccuracy88.06—Unverified
3ArcFaceAccuracy87.95—Unverified
4Fine-tuned VGG-FaceAccuracy86.85—Unverified
5FaceNetAccuracy84.21—Unverified
6VGG-FaceAccuracy79.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Fine-tuned ArcFaceAccuracy95.43—Unverified
2Fine-tuned FaceNetAccuracy93.58—Unverified
3ArcFaceAccuracy91.78—Unverified
4Fine-tuned VGG-FaceAccuracy91.51—Unverified
5FaceNetAccuracy90.96—Unverified
6VGG-FaceAccuracy84.56—Unverified
#ModelMetricClaimedVerifiedStatus
1MS1MV2, R100, SFaceAccuracy91.57—Unverified
2MS1MV2, R100, ArcfaceAccuracy90.57—Unverified
3MS1MV2, R100, CurricularfaceAccuracy90.43—Unverified
4VGGFace2, R50, ArcFaceAccuracy85.95—Unverified
5CASIA-WebFace, R50, CosFaceAccuracy82.52—Unverified
6Private-Asia, R50, ArcFaceAccuracy77.2—Unverified
#ModelMetricClaimedVerifiedStatus
1ProdpolyAccuracy0.98—Unverified
2ElasticFace-CosAccuracy0.98—Unverified
3Transformer loss+ArcFace ResNet100Accuracy0.98—Unverified
4DCQAccuracy0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1VGG based5-class test accuracy99.2—Unverified
2PIC - ArcFaceFNMR [%] @ 10-3 FMR4.22—Unverified
3PIC - MagFaceFNMR [%] @ 10-3 FMR3.92—Unverified
4PIC - QMagFaceFNMR [%] @ 10-3 FMR3.24—Unverified
#ModelMetricClaimedVerifiedStatus
1ElasticFace-CosTAR @ FAR=0.00010.95—Unverified
2ArcFace+CSFMRank-10.95—Unverified
3ArcFace + MS1MV2 + R100Rank-10.95—Unverified
4AdaFace + MS1MV3 + R100TAR @ FAR=0.00010.94—Unverified
#ModelMetricClaimedVerifiedStatus
1ProdpolyAccuracy0.96—Unverified
2ElasticFace-ArcAccuracy0.96—Unverified
3GhostFaceNetV2-1Accuracy0.96—Unverified
#ModelMetricClaimedVerifiedStatus
1PIC - ArcFaceFNMR [%] @ 10-3 FMR10.1—Unverified
2PIC - QMagFaceFNMR [%] @ 10-3 FMR9.78—Unverified
3PIC - MagFaceFNMR [%] @ 10-3 FMR9.75—Unverified
#ModelMetricClaimedVerifiedStatus
1ArcFace + PPLTAR@FAR=0.000178.8—Unverified
2MaskInv-HGTAR@FAR=0.000178.36—Unverified
3FocusFaceTAR@FAR=0.000177.77—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaFace+PPLTAR@FAR=0.000184.27—Unverified
2MaskInv-HGTAR@FAR=0.000183.82—Unverified
3FocusFaceTAR@FAR=0.000183.4—Unverified
#ModelMetricClaimedVerifiedStatus
1PIC - QMagFaceFNMR [%] @ 10-3 FMR0.96—Unverified
2PIC - MagFaceFNMR [%] @ 10-3 FMR0.96—Unverified
3PIC - ArcFaceFNMR [%] @ 10-3 FMR0.05—Unverified
#ModelMetricClaimedVerifiedStatus
1FaceNet+Adaptive ThresholdAverage Accuracy (10 times)84.3—Unverified
2FaceNet+Fixed Threshold (0.2487)Average Accuracy (10 times)80.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Model with Up Convolution + DoG Filter (Aligned)Rank-185—Unverified
2DPMRank-171—Unverified
#ModelMetricClaimedVerifiedStatus
1FaceNet+Adaptive ThresholdAverage Accuracy (10 times)83.79—Unverified
2FaceNet+Fixed Threshold (0.3968)Average Accuracy (10 times)80.72—Unverified
#ModelMetricClaimedVerifiedStatus
1GhostFaceNetV2-1Accuracy0.95—Unverified
2ElasticFace-ArcAccuracy0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1FaceNet+Adaptive ThresholdAverage Accuracy (10 times)76.46—Unverified
2FaceNet+Fixed Threshold (0.3779)Average Accuracy (10 times)53.97—Unverified
#ModelMetricClaimedVerifiedStatus
1Wang et al. [5]Rank-194.5—Unverified
2Multi-taskRank-184.32—Unverified
#ModelMetricClaimedVerifiedStatus
1Model with Up Convolution + DoG FilterRank-187.2—Unverified
2DPMRank-183.73—Unverified
#ModelMetricClaimedVerifiedStatus
1MCNTAR @ FAR=0.010.54—Unverified
#ModelMetricClaimedVerifiedStatus
1GhostFaceNetV2-1Accuracy99.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Model with Up Convolution + DoG FilterRank-188.33—Unverified
#ModelMetricClaimedVerifiedStatus
1Partial FCMFR-ALL97.85—Unverified
#ModelMetricClaimedVerifiedStatus
1FaceTransformer+OctupletLossAccuracy0.95—Unverified