https://github.com/danjun6737/transface
공식 코드TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective
Papers with Code 아카이브
Face Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 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/dariant/id-booth
공식 코드ID-Booth: Identity-consistent Face Generation with Diffusion Models
https://github.com/dasec/dataset-duplicates
공식 코드Considerations on the Evaluation of Biometric Quality Assessment Algorithms
https://github.com/dasec/hda-synchildfaces-agetransformation
공식 코드Child Face Recognition at Scale: Synthetic Data Generation and Performance Benchmark
https://github.com/dasec/ladimo
공식 코드LADIMO: Face Morph Generation through Biometric Template Inversion with Latent Diffusion
https://github.com/dasec/tetraloss
공식 코드TetraLoss: Improving the Robustness of Face Recognition against Morphing Attacks
https://github.com/datasciencegrimmer/syn-yawpitch
공식 코드https://github.com/david-svitov/margindistillation
공식 코드https://github.com/davidberend/FairnessMatters
공식 코드Fair and accurate age prediction using distribution aware data curation and augmentation
https://github.com/davidsandberg/facenet
공식 코드https://github.com/deepcam-cn/facequality
공식 코드EQFace: A Simple Explicit Quality Network for Face Recognition
https://github.com/deepinsight/insightface
공식 코드ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/deepinsight/insightface/tree/master/challenges/cvpr23-fas-wild
공식 코드Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc
공식 코드Partial FC: Training 10 Million Identities on a Single Machine
https://github.com/deezer/sigir2019-2stagesampling
공식 코드Improving Collaborative Metric Learning with Efficient Negative Sampling
https://github.com/delaprada/handsynthesis
공식 코드Analyzing the Synthetic-to-Real Domain Gap in 3D Hand Pose Estimation
https://github.com/dgragnaniello/PQP
공식 코드Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers
https://github.com/dituu/Semi-Siamese-Training
공식 코드https://github.com/dooleys/fr-nas
공식 코드Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
https://github.com/dreammr/pcl
공식 코드Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
https://github.com/dxomark-research/piq2023
공식 코드https://github.com/echoanran/on-mitigating-hard-clusters
공식 코드https://github.com/eduardacaldeira/mst-kd
공식 코드MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition
https://github.com/eglxiang/vgg_face
공식 코드https://github.com/eglxiang/ytf
공식 코드https://github.com/ekremerakin/RealWorldOccludedFaces
공식 코드https://github.com/emalach/UpdateByDisagreement
공식 코드https://github.com/face3d0725/FaceExtraction
공식 코드FaceOcc: A Diverse, High-quality Face Occlusion Dataset for Human Face Extraction
https://github.com/fahadshamshad/clip2protect
공식 코드CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search
https://github.com/fahadshamshad/deep-facial-privacy-prior
공식 코드Makeup-Guided Facial Privacy Protection via Untrained Neural Network Priors
https://github.com/fairdeface/FairDeface
공식 코드FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods
https://github.com/faniamokhayeri/C-GAN
공식 코드https://github.com/faniamokhayeri/DSFS
공식 코드Domain-Specific Face Synthesis for Video Face Recognition from a Single Sample Per Person
https://github.com/fatemejamalii/e2f-net
공식 코드https://github.com/faysal-md/unmasking-deepfake-faces-from-videos-an-explainable-cost-sensitive-deep-learning-approach-ieee2023
공식 코드Unmasking Deepfake Faces from Videos Using An Explainable Cost-Sensitive Deep Learning Approach
https://github.com/fbiying87/demographic-bias-visualization
공식 코드Towards Explaining Demographic Bias through the Eyes of Face Recognition Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.