https://github.com/senguptaumd/SfSNet
An All-In-One Convolutional Neural Network for Face Analysis
Papers with Code 아카이브
Face Recognition
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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/serengil/deepface
Deep Learning Face Representation by Joint Identification-Verification
https://github.com/setharram/facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/sevenHsu/Face_Recognition_IN_Video
https://github.com/shahidul56/Tiny_Faces_in_Tensorflow_msc
https://github.com/shi510/ffem
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/shiqiyu/libfacedetection.train
https://github.com/shridharrhegde/vgg-age
Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model
https://github.com/shuShelby/main
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/shyhyawJou/ArcFace-Pytorch
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/sicxu/Deep3DFaceRecon_pytorch
https://github.com/sithu31296/EasyFace
https://github.com/slawekslex/ArcFace_Explained
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/smkim7-kr/toy-projects/tree/master/ArcFace-visualization
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/soumik12345/Nearest-Celebrity-Face
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/srilakshmiv14/FaceNet_Pytorch
https://github.com/stdereka/knowledge-distillation
An Embarrassingly Simple Approach for Knowledge Distillation
https://github.com/stresearch/xfr
https://github.com/sudhanshu2k/Signature-Verification
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/sumathi16/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/sunil-rival/insightface
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/susmith98/Face_Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/suyash0612/face_recognitionandverification
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/tamerthamoqa/facenet-pytorch-glint360k
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/tamnil/esp32-xtensa-study
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/taylorlu/ghostvlad-speaker
https://github.com/tbmoon/facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/tencentarc/opencompatible
Towards Universal Backward-Compatible Representation Learning
https://github.com/thongdk8/realtime-facereccpp
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/thu-ml/realsafe
RobFR: Benchmarking Adversarial Robustness on Face Recognition
https://github.com/timesler/facenet-pytorch
https://github.com/tomas-gajarsky/facetorch
Pre-training strategies and datasets for facial representation learning
https://github.com/townim-faisal/Revisiting-Pixel-Wise-Supervision-for-Face-Anti-Spoofing
https://github.com/trachpro/arcface-tf2
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/trachpro/insightFace_v2
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/tueboesen/Active-Learning
Cost-Effective Active Learning for Deep Image Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.