https://gitlab.idiap.ch/bob/bob.paper.ijcb2021_synthetic_dataset
공식 코드On the use of automatically generated synthetic image datasets for benchmarking face recognition
Papers with Code 아카이브
Facial Recognition and Modelling
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Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://gitlab.idiap.ch/bob/bob.paper.ijcb2023_caim_hfr
공식 코드Bridging the Gap: Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation
https://gitlab.idiap.ch/bob/bob.paper.ijcb2023_synthdistill
공식 코드SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data
https://gitlab.idiap.ch/bob/bob.paper.ijcb2024_agnostic_features_mad
공식 코드Evaluating the Effectiveness of Attack-Agnostic Features for Morphing Attack Detection
https://gitlab.idiap.ch/bob/bob.paper.tifs2022_hfr_prepended_domain_transformer
공식 코드Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles
https://huggingface.co/FoivosPar/Arc2Face
공식 코드https://worksheets.codalab.org/worksheets/0x7b8f6fbc6b5c49c18ac7ca94aafaa1a7
공식 코드https://bitbucket.org/nishkalavallabhi/complexity-features
Readability-based Sentence Ranking for Evaluating Text Simplification
https://dagshub.com/Bharat-mtr/LightCNN
https://gitee.com/gai-shaoyan/mind3d
Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
https://github.com/163GitHub/AI
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/18alantom/identify
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/18alantom/smel_project
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/1996scarlet/ArcFace-Multiplex-Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/1996scarlet/Laser-Eye
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/1996scarlet/dense-head-pose-estimation
https://github.com/1996scarlet/faster-mobile-retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/1adrianb/2D-and-3D-face-alignment
https://github.com/1adrianb/binary-face-alignment
https://github.com/1adrianb/binary-human-pose-estimation
https://github.com/1adrianb/face-alignment-training
https://github.com/1chenchen22/LFNSB
https://github.com/1ho0jin1/QMUL-SurvFace-Open-Set-Identification
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/ADNet
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/2023-MindSpore-1/ms-code-162
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/2023-MindSpore-1/ms-code-177
https://github.com/2023-MindSpore-1/ms-code-178
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/2023-MindSpore-1/ms-code-18/tree/main/LightCNN
https://github.com/2023-MindSpore-1/ms-code-18/tree/main/MaskedFaceRecognition
https://github.com/2023-MindSpore-1/ms-code-18/tree/main/lresnet100e_ir
https://github.com/2023-MindSpore-1/ms-code-201
https://github.com/2023-MindSpore-1/ms-code-208
https://github.com/2023-MindSpore-1/ms-code-211/tree/main/DeepID
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceAttribute
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceDetection
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceNet
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.