https://github.com/PuchatekwSzortach/face_detection
Multi-view Face Detection Using Deep Convolutional Neural Networks
Papers with Code 아카이브
3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Pulkitdzrt/ML-Image-Super-Resolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation
https://github.com/PushpakBhoge/Face_Recognition_TF
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Puzer/stylegan-encoder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Qengineering/Face-Mask-Detection-Jetson-Nano
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/Qengineering/Face-Mask-Detection-Raspberry-Pi-64-bits
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/Qengineering/Face-detection-Raspberry-Pi-32-64-bits
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/Qengineering/LFFD-MNN-Jetson-Nano
https://github.com/Qengineering/LFFD-MNN-Raspberry-Pi-4
https://github.com/Qengineering/LFFD-ncnn-Jetson-Nano
https://github.com/Qengineering/LFFD-ncnn-Raspberry-Pi-4
https://github.com/QianliM/CAPE
https://github.com/QuocThangNguyen/deep-metric-learning-tsinghua-dogs
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/RAGHAV2998/Everybody-Can-Dance-Now-Video-game-version-
https://github.com/RainbowRui/Landmark-Driven-Facial-Expression-Recognition
Suppressing Uncertainties for Large-Scale Facial Expression Recognition
https://github.com/Raj-08/Deepfake-Detection-Mesonet
https://github.com/Rakshit-Shetty/Resnet-Implementation
https://github.com/Ram81/AC-VAEGAN-PyTorch
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
https://github.com/Recognito-Vision/Face-SDK-Android-Demo
Unrecognizable Yet Identifiable: Image Distortion with Preserved Embeddings
https://github.com/Recognito-Vision/Face-SDK-Linux-Demos
Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection
https://github.com/RezisEwig/esrgan
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/RiS3-Lab/ModelXRay
https://github.com/RicardooYoung/LatentDistributionAdjusting
https://github.com/RichardMathewsII/YOLBO
https://github.com/RobotEdh/facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/RobotMobile/cv-deep-learning-paper-review
https://github.com/RocketFlash/easy_metric_learning/tree/master/tools
https://github.com/Rohed/ml-1
https://github.com/RomanticWithoutStatus/PFLD-TensorFlow2caffe
https://github.com/RoninTheKid/yessir
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/Rosie-Brigham/sesmeg
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/Rubikplayer/flame-fitting
https://github.com/Rudedaisy/attention-is-all-you-need-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.