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/M155K4R4/facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/MIC-DKFZ/DetectionAndRegression
https://github.com/MIC-DKFZ/RegRCNN
https://github.com/Maninder10/face
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/MariefW/6.-trainingYOLO
https://github.com/Maskify/darknet
https://github.com/MatGod7/Retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/MegEngine/Models/tree/master/official/vision/detection
https://github.com/Mind23-2/MindCode-101/tree/main/LightCNN
https://github.com/Mind23-2/MindCode-13
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Mind23-2/MindCode-176
https://github.com/Mind23-2/MindCode-3/tree/main/LightCNN
https://github.com/Mind23-2/MindCode-56
https://github.com/Mind23-2/MindCode-70
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Mind23-2/MindCode-71
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/Mind23-2/MindCode-95
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/MindCode-4/code-11/tree/main/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/MindCode-4/code-6/tree/main/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/MindSpore-paper-code-3/code2/tree/main/DeepID
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceDetection
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/MindSpore-paper-code-3/code5/tree/main/retinanet_resnet101
https://github.com/MindSpore-paper-code-3/code5/tree/main/retinanet_resnet152
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceDetection
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/MindSpore-paper-code-3/code8/tree/main/DeepID
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceDetection
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
https://github.com/MindSpore-paper-code-3/code8/tree/main/faceboxes
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/MindSpore-paper-code-3/code9/tree/main/RetinaFace_ResNet50
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/MindSpore-scientific-2/code-11/tree/main/SV-X-Softmax
https://github.com/MindSpore-scientific/code-3/tree/main/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/MohamadMerchant/Voice-Authentication-and-Face-Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Mrnoorsingh/QRCode-and-Face-Recognition-for-Access-Control
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Mrzhang3389/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/NVIDIA/retinanet-examples
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.