https://github.com/KikimorMay/MultiFace
공식 코드MultiFace: A Generic Training Mechanism for Boosting Face Recognition Performance
Papers with Code 아카이브
Face Recognition
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- 보존된 평가 기록
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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/KnowledgeDiscovery/FaceSec
공식 코드FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems
https://github.com/KovenYu/state-information
공식 코드Weakly supervised discriminative feature learning with state information for person identification
https://github.com/LSIbabnikz/DifFIQA
공식 코드DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models
https://github.com/Leminhbinh0209/KappaFace
공식 코드KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition
https://github.com/MSoumm/FaVFA
공식 코드Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification
https://github.com/MahmoudRabea13/IdentiFace
공식 코드https://github.com/Martlgap/btm-stm
공식 코드Susceptibility to Image Resolution in Face Recognition and Trainings Strategies
https://github.com/Martlgap/xqlfw
공식 코드https://github.com/Megatvini/DeepFaceForgeryDetection
공식 코드https://github.com/MelWe/hopf-recognition
공식 코드https://github.com/NetoPedro/FocusFace
공식 코드FocusFace: Multi-task Contrastive Learning for Masked Face Recognition
https://github.com/PRALabBiometrics/APhotoEverydayDB
공식 코드Are Adaptive Face Recognition Systems still Necessary? Experiments on the APE Dataset
https://github.com/PaddlePaddle/PaddleClas
공식 코드https://github.com/Pei-KaiHuang/AAAI25-SLIP
공식 코드SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining
https://github.com/Puspitamajumdariiit/Unravelling-the-Effect-of-Image-Distortions
공식 코드https://github.com/QingyuZhao/BR-Net
공식 코드Representation Learning with Statistical Independence to Mitigate Bias
https://github.com/RameshaKaru/devi
공식 코드DEVI: Open-source Human-Robot Interface for Interactive Receptionist Systems
https://github.com/Raul2718/FLUXSynID
공식 코드https://github.com/RenMin1991/SIDE
공식 코드Artificial Immune System of Secure Face Recognition Against Adversarial Attacks
https://github.com/Rohith-hacker/realtime-faceid
공식 코드https://github.com/RoyYuchenHe/BlockSparse
공식 코드https://github.com/SCL-UT/face-manifold
공식 코드Face Manifold: Manifold Learning for Synthetic Face Generation
https://github.com/SCUT-AILab/DCP
공식 코드Discrimination-aware Network Pruning for Deep Model Compression
https://github.com/Shawn-Shan/fawkes
공식 코드Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
https://github.com/Shawn-Shan/trapdoor
공식 코드Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
https://github.com/ShawnXYang/Face-Robustness-Benchmark
공식 코드RobFR: Benchmarking Adversarial Robustness on Face Recognition
https://github.com/SheepBreedingLab-HZAU/LAD-RCNN
공식 코드LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization
https://github.com/TaharChettaoui/FRoundation
공식 코드FRoundation: Are Foundation Models Ready for Face Recognition?
https://github.com/Tencent/TFace
공식 코드SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance
https://github.com/Tencent/TFace/tree/Adv-Attack-Defense/security/tasks/Adv-Attack-Defense/Sibling-Attack
공식 코드Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition
https://github.com/Varat7v2/adaptive-threshold
공식 코드Adaptive Threshold for Online Object Recognition and Re-identification Tasks
https://github.com/WakingHours-GitHub/EPL
공식 코드https://github.com/Wayfear/Autotune
공식 코드Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues
https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset
공식 코드https://github.com/XiaohangZhan/cdp
공식 코드Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.