https://github.com/mvillarreal14/confoc
공식 코드ConFoc: Content-Focus Protection Against Trojan Attacks on Neural Networks
Papers with Code 아카이브
Face Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/mysee1989/GraphJigsaw
공식 코드https://github.com/naserdamer/mordiff
공식 코드https://github.com/natanielruiz/dockerface
공식 코드Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container
https://github.com/ndido98/frcsyn
공식 코드FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
https://github.com/ndrwmlnk/ai-blitz-xiii
공식 코드https://github.com/ndrwmlnk/deep-face-generation-and-editing-a-survey
공식 코드https://github.com/netopedro/idistill
공식 코드Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection
https://github.com/netopedro/ocfr-2022
공식 코드https://github.com/netopedro/orthomad
공식 코드OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement
https://github.com/niccobiondi/cores-compatibility
공식 코드https://github.com/nkjcqvcpi/asr
공식 코드https://github.com/noboevbo/pedrec
공식 코드PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation
https://github.com/ntubiolin/xcos
공식 코드xCos: An Explainable Cosine Metric for Face Verification Task
https://github.com/nvedant07/Fairness-Through-Robustness
공식 코드Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning
https://github.com/nz0001na/mad_maf
공식 코드Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting
https://github.com/omggggg/mmdg
공식 코드Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
https://github.com/opencv/openvino_training_extensions
공식 코드https://github.com/otroshi/edgeface
공식 코드https://github.com/otroshi/synthdistill
공식 코드SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data
https://github.com/panasonicconnect/invreg
공식 코드https://github.com/papermsucode/fullbboxrestoration
공식 코드Darker than Black-Box: Face Reconstruction from Similarity Queries
https://github.com/parham1998/facial-privacy-protection
공식 코드Enhancing Facial Privacy Protection via Weakening Diffusion Purification
https://github.com/phd-research-ai/tca2
공식 코드Transferable Adversarial Face Attack with Text Controlled Attribute
https://github.com/ppolon/dcp
공식 코드Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition
https://github.com/prevso1088/open-set-biometrics
공식 코드https://github.com/pterhoer/ExplainableFaceImageQuality
공식 코드Pixel-Level Face Image Quality Assessment for Explainable Face Recognition
https://github.com/pterhoer/FairScoreNormalization
공식 코드Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization
https://github.com/pterhoer/MAAD-Face
공식 코드MAAD-Face: A Massively Annotated Attribute Dataset for Face Images
https://github.com/pterhoer/PrivacyPreservingFaceRecognition
공식 코드Unsupervised Enhancement of Soft-biometric Privacy with Negative Face Recognition
https://github.com/pterhoer/QMagFace
공식 코드QMagFace: Simple and Accurate Quality-Aware Face Recognition
https://github.com/pterhoer/optimalmatchingconfidence
공식 코드https://github.com/radekd91/emoca
공식 코드https://github.com/radu-dogaru/LB-CNN-compact-and-fast-binary-including-very-fast-ELM
공식 코드https://github.com/rafaelmmamede/fairness_ocfr
공식 코드Fairness Under Cover: Evaluating the Impact of Occlusions on Demographic Bias in Facial Recognition
https://github.com/rafaelvareto/maximal-entropy-loss
공식 코드Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.