Papers with Code 아카이브
Face Verification
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Face Verification is a machine learning task in computer vision that involves determining whether two facial images belong to the same person or not. The task involves extracting features from the facial images, such as the shape and texture of the face, and then using these features to compare and verify the similarity between the images.
<span style="color:grey; opacity: 0.6">( Image credit: Pose-Robust Face Recognition via Deep Residual Equivariant Mapping )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/chenggongliang/arcface
VGGFace2: A dataset for recognising faces across pose and age
https://github.com/chenyeheng/SmartCar
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/chenyeheng/SmartCar-FaceRec
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/chenyeheng/SmartCar-FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/chrisqqq123/FA-Dist-EfficientNet
https://github.com/chroniccrash/facestuff
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/cindyloo/rockstar-ai
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/clcarwin/sphereface
https://github.com/clcarwin/sphereface_pytorch
https://github.com/cloudnine148/PHC_2nd_SPC
https://github.com/cmusatyalab/openface
https://github.com/code-implementation1/Code1/tree/main/DeepID
https://github.com/code-implementation1/Code3/tree/main/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/code-implementation1/Code3/tree/main/FaceRecognitionForTracking
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/code-implementation1/Code4/tree/main/LightCNN
https://github.com/code-implementation1/Code8/tree/main/sphereface
https://github.com/code-kage/facenet_mini
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/code-trip/insightface-mxnet
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/comratvlad/sv_softmax
https://github.com/coronnie/HouseGuard
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/csm-kr/Retinanet_pytorch
https://github.com/cvqluu/Additive-Margin-Softmax-Loss-Pytorch
https://github.com/cvqluu/Angular-Penalty-Softmax-Losses-Pytorch
https://github.com/cvtower/SeesawNet-pytorch-reimplement
SeesawFaceNets: sparse and robust face verification model for mobile platform
https://github.com/cvtower/SeesawNet_pytorch
SeesawFaceNets: sparse and robust face verification model for mobile platform
https://github.com/cvtower/seesawfacenet_pytorch
VGGFace2: A dataset for recognising faces across pose and age
https://github.com/cydonia999/VGGFace2-pytorch
VGGFace2: A dataset for recognising faces across pose and age
https://github.com/dalisson/am_softmax
https://github.com/darkAlert/retinafacert
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/darshans0200/YOLOTest
https://github.com/daveboat/pytorch_focal_loss
https://github.com/davidsandberg/facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/davisking/dlib
https://github.com/dc17540/darknet
https://github.com/deepakks1995/inception
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.