Papers with Code 아카이브
Face Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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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/humbleOldSage/Triplet-Loss-for-Personal-dataset
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/huyhieupham/3D-Face-Recognition
Learning from Millions of 3D Scans for Large-scale 3D Face Recognition
https://github.com/i6092467/diff-bias-proxies
https://github.com/iPRoBe-lab/semi-adversarial-networks
Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
https://github.com/iacopomasi/face_specific_augm
https://github.com/ibendrup/MulticolumnNetwork
https://github.com/iitmcvg/Demo-of-face-detection-and-counting-
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/ishspsy/project
Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models
https://github.com/itanish/Face-Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/ivclab/cvs
https://github.com/ivychill/insightface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/iwasakishuto/DeepScreening
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/jadevaibhav/Signature-verification-using-deep-learning
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/jankolf/ser-fiq-pytorch
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
https://github.com/javier-hernandezo/faceqgen
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
https://github.com/javier-hernandezo/faceqnet
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
https://github.com/jchaykow/AGN-pytorch
A General Framework for Adversarial Examples with Objectives
https://github.com/jesperhauch/polynomial_deep_learning
https://github.com/jfthuong/photo-organizer
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/jjmachan/DeepHash
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/jnhwkim/cbp
https://github.com/jonathandrnd/Face-Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/joovvhan/ecapa-tdnn
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/jooyounghun/AI-Team-5
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/kagami/go-face
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/kaichengyan/facenet-darknet-pytorch
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/kakaoenterprise/BroadFace
BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition
https://github.com/keetsky/Net_ghostVLAD-pytorch
https://github.com/khrlimam/facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/kim1102/PPL-MFR
https://github.com/kkelchte/task_free_continual_learning
https://github.com/kooBH/facenet_verification
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/krantirk/Face-detection-and-counting-
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/ktjonsson/keras-ArcFace
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/kuaikuaikim/DFace
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.