https://gitlab.idiap.ch/bob/bob.paper.icassp2021_morph
공식 코드Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks
Papers with Code 아카이브
3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://gitlab.idiap.ch/bob/bob.paper.icassp2022_morph_generate
공식 코드https://gitlab.idiap.ch/bob/bob.paper.ijcb2021_synthetic_dataset
공식 코드On the use of automatically generated synthetic image datasets for benchmarking face recognition
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
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.