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Face Recognition

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하위 태스크: 6
평가표: 25
보존된 평가 기록: 88

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.

<span style="color:grey; opacity: 0.6">( Image credit: Face Verification )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/ydwen/opensphere/blob/main/opensphere/module/head/simple.py

공식 코드

Pairwise Similarity Learning is SimPLE

https://github.com/yinhefeng/LCLRRDL

공식 코드

Face Recognition via Locality Constrained Low Rank Representation and Dictionary Learning

https://github.com/yinhefeng/soigo

공식 코드

Face recognition via compact second order image gradient orientations

https://github.com/yl-1993/learn-to-cluster

공식 코드

Learning to Cluster Faces on an Affinity Graph

https://github.com/yliuaa/bnmr-faircompface

공식 코드

Component-Based Fairness in Face Attribute Classification with Bayesian Network-informed Meta Learning

https://github.com/yule-li/MassFace

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MassFace: an efficient implementation using triplet loss for face recognition

https://github.com/yuleung/mixbct

공식 코드

MixBCT: Towards Self-Adapting Backward-Compatible Training

https://github.com/yulu0724/EmbeddingDistillation

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Learning Metrics from Teachers: Compact Networks for Image Embedding

https://github.com/zamdimon/distortion-generator

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Unrecognizable Yet Identifiable: Image Distortion with Preserved Embeddings

https://github.com/zblasingame/DiM

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Leveraging Diffusion For Strong and High Quality Face Morphing Attacks

https://github.com/zblasingame/Greedy-DiM

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Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs

https://github.com/zhao-zilong/MotivationCaseStudies

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Enhancing Robustness of On-line Learning Models on Highly Noisy Data

https://github.com/zhaoyuzhi/childpredictor

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ChildPredictor: A Child Face Prediction Framework with Disentangled Learning

https://github.com/zhelishisongjie/ssfer

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Bridging the Gaps: Utilizing Unlabeled Face Recognition Datasets to Boost Semi-Supervised Facial Expression Recognition

https://github.com/zhenglisec/decision-based-mia

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Membership Leakage in Label-Only Exposures

https://github.com/zhiqic/keyposs

공식 코드

KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration

https://github.com/zhongyy/Adversarial_MTER

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Adversarial Learning with Margin-based Triplet Embedding Regularization

https://github.com/zhongyy/Face-Transformer

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Face Transformer for Recognition

https://github.com/zhongyy/SFace

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SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition

https://github.com/zhongyy/opom

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OPOM: Customized Invisible Cloak towards Face Privacy Protection

https://github.com/zitongyu/flex-modal-fas

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Flexible-Modal Face Anti-Spoofing: A Benchmark

https://github.com/zma-c-137/VarGFaceNet

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VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition

https://github.com/zpguigroupwhu/meancut-clustering

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MeanCut: A Greedy-Optimized Graph Clustering via Path-based Similarity and Degree Descent Criterion

https://gitlab.idiap.ch/biometric/icassp2024.dvpf

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Deep Variational Privacy Funnel: General Modeling with Applications in Face Recognition

https://gitlab.idiap.ch/bob/bob.paper.backdoors_anomaly_detection.biosig2022

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An anomaly detection approach for backdoored neural networks: face recognition as a case study

https://gitlab.idiap.ch/bob/bob.paper.eusipco2023_mlphash

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MLP-Hash: Protecting Face Templates via Hashing of Randomized Multi-Layer Perceptron

https://gitlab.idiap.ch/bob/bob.paper.icassp2021_morph

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Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks

https://gitlab.idiap.ch/bob/bob.paper.icassp2022_morph_generate

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Are GAN-based Morphs Threatening Face Recognition?

https://gitlab.idiap.ch/bob/bob.paper.ijcb2021_synthetic_dataset

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On the use of automatically generated synthetic image datasets for benchmarking face recognition

https://gitlab.idiap.ch/bob/bob.paper.ijcb2023_caim_hfr

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Bridging the Gap: Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation

https://gitlab.idiap.ch/bob/bob.paper.ijcb2023_synthdistill

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SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data

https://gitlab.idiap.ch/bob/bob.paper.ijcb2024_agnostic_features_mad

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Evaluating the Effectiveness of Attack-Agnostic Features for Morphing Attack Detection

https://gitlab.idiap.ch/bob/bob.paper.tifs2022_hfr_prepended_domain_transformer

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Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles

https://huggingface.co/FoivosPar/Arc2Face

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Arc2Face: A Foundation Model for ID-Consistent Human Faces

https://dagshub.com/Bharat-mtr/LightCNN

A Light CNN for Deep Face Representation with Noisy Labels

https://github.com/18alantom/identify

FaceNet: A Unified Embedding for Face Recognition and Clustering

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.