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/barisgecer/facegan
공식 코드https://github.com/barulalithb/stereo-angular-margin
공식 코드Improvising the Learning of Neural Networks on Hyperspherical Manifold
https://github.com/bghojogh/Face-Gender-Recognition-Fusion
공식 코드A Fusion-based Gender Recognition Method Using Facial Images
https://github.com/bghojogh/Pixel-Aligned-Face-Recognition
공식 코드Pixel-Level Alignment of Facial Images for High Accuracy Recognition Using Ensemble of Patches
https://github.com/bghojogh/Roweis-Discriminant-Analysis
공식 코드Roweis Discriminant Analysis: A Generalized Subspace Learning Method
https://github.com/bghojogh/Roweisposes
공식 코드Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition
https://github.com/bghojogh/Weighted-Fisher-Discriminant-Analysis
공식 코드Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
https://github.com/bieltura/Automotive_face_detector
공식 코드Low cost enhanced security face recognition with stereo cameras
https://github.com/bilylee/DCQ
공식 코드Dynamic Class Queue for Large Scale Face Recognition In the Wild
https://github.com/bjzhb666/GS-LoRA
공식 코드https://github.com/bovifocr/tcdiff
공식 코드TCDiff: Triple Condition Diffusion Model with 3D Constraints for Stylizing Synthetic Faces
https://github.com/brkljac/videoface2.0
공식 코드https://github.com/bsi-ofiq/ofiq-project
공식 코드Deep Learning-based Compression Detection for explainable Face Image Quality Assessment
https://github.com/bunni90/facepastingattack
공식 코드https://github.com/caltechvisionlab/frt-rapid-test
공식 코드A Rapid Test for Accuracy and Bias of Face Recognition Technology
https://github.com/canpeng123/fscil_alice
공식 코드Few-Shot Class-Incremental Learning from an Open-Set Perspective
https://github.com/cgcl-codes/amt-gan
공식 코드Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfer
https://github.com/chaochao-lin/atr-fas
공식 코드Enhancing Mobile Face Anti-Spoofing: A Robust Framework for Diverse Attack Types under Screen Flash
https://github.com/charsyme/Human-Data-Generation-Framework
공식 코드Efficient Realistic Data Generation Framework leveraging Deep Learning-based Human Digitization
https://github.com/chenqikong/m3fas
공식 코드M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System
https://github.com/chenzhe207/FDLSR
공식 코드Fisher Discriminative Least Squares Regression for Image Classification
https://github.com/choapinus/densenet10
공식 코드On the Feasibility of Creating Iris Periocular Morphed Images
https://github.com/christianbv/AgeNet
공식 코드https://github.com/cleardusk/3DDFA_V2
공식 코드https://github.com/cleardusk/MFR
공식 코드https://github.com/cleardusk/MeGlass
공식 코드https://github.com/clferrari/facepartsswap
공식 코드What makes you, you? Analyzing Recognition by Swapping Face Parts
https://github.com/clks-wzz/FAS-SGTD
공식 코드Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
https://github.com/clks-wzz/epcr
공식 코드https://github.com/coolEphemeroptera/AESRC2020
공식 코드https://github.com/csehong/SSPP-DAN
공식 코드SSPP-DAN: Deep Domain Adaptation Network for Face Recognition with Single Sample Per Person
https://github.com/csong27/ml-model-remember
공식 코드https://github.com/csxmli2016/SymmFCNet
공식 코드https://github.com/cvgmi/nestedgrassmann
공식 코드Nested Grassmannians for Dimensionality Reduction with Applications
https://github.com/daniel3303/storyreasoning
공식 코드StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.