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Face Verification On Ijb A

평가 지표

TAR @ FAR=0.01

평가 결과

이 벤치마크에서 각 모델의 성능 결과

		Paper Title
Dual-Agent GANs	97.60%	Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis
PFEfuse + match	97.5%	Probabilistic Face Embeddings
SE-GV-4-g1	97.2%	GhostVLAD for set-based face recognition
L2-constrained softmax loss	97%	L2-constrained Softmax Loss for Discriminative Face Verification
VGGFace2_ft	96.8%	VGGFace2: A dataset for recognising faces across pose and age
StyleFNM	94.60%	Inclusive normalization of face images to passport format
Deep Residual Equivariant Mapping	94.40%	Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
NAN	94.10%	Neural Aggregation Network for Video Face Recognition
Template adaptation	93.90%	Template Adaptation for Face Verification and Identification
All-in-one CNN	92.20%	An All-In-One Convolutional Neural Network for Face Analysis
FPN	90.1%	FacePoseNet: Making a Case for Landmark-Free Face Alignment
Triplet probabilistic embedding	90%	Triplet Probabilistic Embedding for Face Verification and Clustering
Synthesis as data augmentation	88.60%	Do We Really Need to Collect Millions of Faces for Effective Face Recognition?
DCNN	83.80%	Unconstrained Face Verification using Deep CNN Features
Deep multi-pose representations	78.70%	Face Recognition Using Deep Multi-Pose Representations
Deep CNN + COTS matcher	73.30%	Face Search at Scale: 80 Million Gallery
VGG + GANFaces	53.507%	Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model

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Face Verification On Ijb A | SOTA | HyperAI초신경