Papers with Code 아카이브
Face Recognition
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- 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/lwkjjonak/quantum_neural_network_classifiers
공식 코드https://github.com/lxq1000/faceptor
공식 코드https://github.com/lxq1000/swinface
공식 코드https://github.com/lyp-deeplearning/mos-multi-task-face-detect
공식 코드https://github.com/machine-listeners-valencia/fsl_osr_dataset_baseline
공식 코드https://github.com/mahmoods01/agns
공식 코드A General Framework for Adversarial Examples with Objectives
https://github.com/mantonios107/attrs-fr-embs
공식 코드Attributes Shape the Embedding Space of Face Recognition Models
https://github.com/mapooon/blendface
공식 코드https://github.com/marcelowds/sr-sde
공식 코드Face Super-Resolution Using Stochastic Differential Equations
https://github.com/marcohuber/xssab
공식 코드Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation
https://github.com/martlgap/human-machine-fusion
공식 코드Tackling Face Verification Edge Cases: In-Depth Analysis and Human-Machine Fusion Approach
https://github.com/martlgap/octuplet-loss
공식 코드Octuplet Loss: Make Face Recognition Robust to Image Resolution
https://github.com/martlgap/x-face-verification
공식 코드Explainable Model-Agnostic Similarity and Confidence in Face Verification
https://github.com/matteoferrara/morphing-attack-potential
공식 코드https://github.com/mcrl/udc-sit
공식 코드UDC-VIT: A Real-World Video Dataset for Under-Display Cameras
https://github.com/mdswyz/SISN-Face-Hallucination
공식 코드Face Hallucination via Split-Attention in Split-Attention Network
https://github.com/mehryaragha/NoseBiometrics
공식 코드Nasal Patches and Curves for Expression-robust 3D Face Recognition
https://github.com/meilfang/fairnessfacepad
공식 코드https://github.com/meilfang/lmfd-pad
공식 코드https://github.com/meilfang/spl-mad
공식 코드Unsupervised Face Morphing Attack Detection via Self-paced Anomaly Detection
https://github.com/meilfang/synthaspoof
공식 코드SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data
https://github.com/michaeltrs/rethinknirvis
공식 코드https://github.com/microsoft/auto-formula
공식 코드https://github.com/microsoft/digiface1m
공식 코드DigiFace-1M: 1 Million Digital Face Images for Face Recognition
https://github.com/milliema/invreg
공식 코드https://github.com/mintelligence-group/visiophysioenet
공식 코드VisioPhysioENet: Multimodal Engagement Detection using Visual and Physiological Signals
https://github.com/mk-minchul/adaface
공식 코드https://github.com/mk-minchul/caface
공식 코드Cluster and Aggregate: Face Recognition with Large Probe Set
https://github.com/mk-minchul/dcface
공식 코드DCFace: Synthetic Face Generation with Dual Condition Diffusion Model
https://github.com/mk-minchul/kprpe
공식 코드https://github.com/modelscope/facechain
공식 코드TopoFR: A Closer Look at Topology Alignment on Face Recognition
https://github.com/mohammadrezanaderi4/sfi-swin
공식 코드https://github.com/msed-ebrahimi/aroface
공식 코드ARoFace: Alignment Robustness to Improve Low-Quality Face Recognition
https://github.com/msy1412/abaw4
공식 코드AU-Supervised Convolutional Vision Transformers for Synthetic Facial Expression Recognition
https://github.com/murphytju/difffas
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.