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/clxiao/Tiny_Faces_in_Tensorflow
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-implementation1/Code9/tree/main/Arcface
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/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
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/cydonia999/Tiny_Faces_in_Tensorflow
https://github.com/cydonia999/VGGFace2-pytorch
VGGFace2: A dataset for recognising faces across pose and age
https://github.com/decile-team/cords
https://github.com/deepakks1995/inception
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/deepinx/SDU_face_alignment
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
https://github.com/deepinx/ShuffleNet_v1_and_v2
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
https://github.com/deepinx/iqiyi-vid-challenge
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/deepinx/sdu-face-alignment
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
https://github.com/deepinx/shufflenet-v1-and-v2
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
https://github.com/dhaalves/CEAL_keras
Cost-Effective Active Learning for Deep Image Classification
https://github.com/didi/AoE
SeesawFaceNets: sparse and robust face verification model for mobile platform
https://github.com/divelab/vqa-text
https://github.com/dmonterom/face_recognition_TF2
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/doantienthongbku/Implementation-patchnet
PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition
https://github.com/douglas125/SpeechIdentity
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/drewbarot/Un-CNN
A General Framework for Adversarial Examples with Objectives
https://github.com/dude123studios/SmarterRingV2
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/dvndra/face_recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/e-solutions-GmbH/TensorFlow-iEVM
Exploring the Open World Using Incremental Extreme Value Machines
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.