Papers with Code 아카이브
Face Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 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/Vill-Lab/2023-TIFS-DTIBA
FastReID: A Pytorch Toolbox for General Instance Re-identification
https://github.com/Vishal-V/GSoC-TensorFlow-2019/blob/master/face_app/model.py
https://github.com/WeidiXie/Keras-VGGFace2-ResNet50
VGGFace2: A dataset for recognising faces across pose and age
https://github.com/XuyangBai/D3Feat
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/YadiraF/DECA
VGGFace2: A dataset for recognising faces across pose and age
https://github.com/Yang-Q-D/mindsporeCode/tree/master/arcface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/ZhaoJ9014/face.evoLVe
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/aayushdD/Face_recognition-Keras-openface
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/abhiexo/Deep_Learning-2
https://github.com/abuchin/face_recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/adambielski/siamese-triplet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/aditya26-09/Face_Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/afrazsalim/WebBasedGameWithNodeJs
VGGFace2: A dataset for recognising faces across pose and age
https://github.com/agrawalparth10/Face-is-the-Key
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/ahmedbesbes/whales-classification
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/aiinai/tf-insightface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/akirafukui/vqa-mcb
https://github.com/akshaybahadur21/Facial-Recognition-using-Facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/akshayraghavan21/Face_Recognition_Using_Facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/albanie/mcnCrossModalEmotions
VGGFace2: A dataset for recognising faces across pose and age
https://github.com/alexattia/ExtendedTinyFaces
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/alibaba/EasyCV
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/alililia/ascend_sphereface
https://github.com/alililia/gpu_sphereface
https://github.com/altndrr/persona
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/altomator/Introduction_to_Deep_Learning-2-Face_Detection
https://github.com/amanshenoy/pose-invariant-face-recognition
https://github.com/anhttran/3dmm_basic
Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network
https://github.com/anhttran/3dmm_cnn
Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network
https://github.com/anhtu293/Vietnamese-Celebrity-Face-Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/aniket03/keras_compact_bilnear_CNN
https://github.com/anjith2006/bob.paper.mcae.icb2019
Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing
https://github.com/anjith2006/bob.paper.mccnn.tifs2018
Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network
https://github.com/anjith2006/bob.paper.oneclass_mccnn_2019
https://github.com/anjith2006/bob.paper.tbiom2023_edgeface
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.