Awesome
Face Related Papers and Code Collection
Any face research/engineer related merge request is wellcome! 02/08/2019.
Index
- Toolkits
- Face Detection
- Face Alignment
- Face Recosntruction
- Face Recognition
- Face Generation
- Face Attributes Analysis
Toolkits <a name="toolkit"></a>
- FaRE: Open Source Face Recognition Performance Evaluation Package [Paper] [Code is coming soon!]
- Gluon Toolkit for Face Recognition [MXNET]
- Deep Learning:
- MXNet and Gluon: A flexible and efficient library for deep learning.
- Torch and PyTorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration.
- TensorFlow: An open-source software library for Machine Intelligence.
- Caffe and Caffe2: A lightweight, modular, and scalable deep learning framework.
- Machine Learning:
- Dlib: A machine learning toolkit.
- Computer Vision:
- OpenCV: Open Source Computer Vision Library.
- Probabilistic Programming
- Pyro: Deep universal probabilistic programming with Python and PyTorch
Face Detection <a name="face-detection"></a>
Survey <a name="face-detection-survey"></a>
Datasets <a name="face-detection-datasets"></a>
- Wildest Faces: Face Detection and Recognition in Violent Settings
- WIDER FACE: A Face Detection Benchmark [Project]
- FDDB: Face Detection and Data Set Benchmark [Project]
- AFLW: Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization [Project]
Research <a name="face-detection-research"></a>
- PyramidBox: A Context-assisted Single Shot Face Detector [ Paper] [TensorFlow] [PyTorch] [MXNet]
- Face Attention Network: An Effective Face Detector for the Occluded Faces [Paper] [PyTorch]
- FaceNess-Net: Face Detection through Deep Facial Part Responses: [Paper]
- S<sup>3</sup>FD: Single Shot Scale-invariant Face Detector [Paper] [Caffe] [PyTorch]
- Finding Tiny Faces: [Project] [Paper] [MatConvNet + MATLAB] [TensorFlow] [MXNET]
- SSH: Single Stage Headless Face Detector: [Paper] [Caffe] [TensorFlow] [MXNET]
- Focal Loss for Dense Object Detection: [Paper] [Caffe] [TensorFlow] [MXNET]
- Face R-CNN: [Paper] [Caffe]
- FaceBoxes: A CPU Real-time Face Detector with High Accuracy [Paper] [Caffe]
- Multiview Face Detection: [Paper] [Caffe]
Face Alignment <a name="face-alignment"></a>
Survey <a name="face-alignment"></a>
Datasets <a name="face-alignment"></a>
- LS3D-W: How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) [Project]
- AFLW: Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization. [Project]
- 300-W [Project]
- 300-VW [Project]
Research <a name="face-alignment"></a>
- FAN: How far are we from solving the 2D & 3D Face Alignment problem? [Paper] [PyTorch]
- JFA: Joint Head Pose Estimation and Face Alignment Framework Using Global and Local CNN Features [Paper]
- MDM: Mnemonic Descent Method [Paper] [TensorFlow]
- RDL: Recurrent 3D-2D Dual Learning for Large-pose Facial Landmark Detection [Paper]
- PIFA: Pose-invariant 3D face alignment [Paper] [Code]
Face Reconstruction <a name="face-reconstruction"></a>
Survey <a name="face-reconstruction-survey"></a>
Datasets <a name="face-reconstruction-datasets"></a>
Research <a name="face-reconstruction-research"></a>
- UH-E2FAR: End-to-end 3D face reconstruction with deep neural networks: [Paper]
- Multi-View 3D Face Reconstruction with Deep Recurrent Neural Networks: [Paper]
- 3D Face Morphable Models "In-the-Wild" [Paper]
- 3DMM-CNN [Paper] [Code]
- VRN [Paper] [Code] [Online Demo]
- 3DFaceNet [Paper]
- MoFA: Unsupervised learning for 3D model and pose parameters [Paper]
- 3DMM-STN: Using 3DMM to transfer 2D image to 2D image texture [Paper]
- Dense Semantic and Topological Correspondence of 3D Faces without Landmarks
- Generating 3D Faces using Convolutional Mesh Autoencoders [Paper] [Code]
Face Recognition <a name="face-recognition"></a>
Survey <a name="face-recognition-survey"></a>
Tutorial <a name="face-recognition-tutorial"></a>
Datasets <a name="face-recognition-datasets"></a>
Training sets:
- MS-Celeb-1M: Microsoft dataset contains around 1M subjects [Project] [Paper]
- CASIA WebFace: 10,575 subjects and 494,414 images [Project] [Paper]
- CelebA: 202,599 images and 10,177 subjects, 5 landmark locations, 40 binary attributes [Project]
- VGG-Face2: A large-scale face dataset contains 3.31 million imaes of 9131 identities. [Project]
Face Verification
- LFW: Labeled Face in the Wild: 13,000 images and 5749 subjects [Download]
- CFP: Celebrities in Frontal-Profile in the Wild [Project] [Paper]
- MegaFace: 1 Million Faces for Recognition at Scale, 690,572 subjects [Download]
- Surveillance Face Recognition Challenge [Project] [Paper]
Face Closed-set Identification
- UHDB31: UHDB31: A Dataset for Better Understanding Face Recognition across Pose and Illumination Variation [Paper]
Face Open-set Identification
- IJB-C: IARPA Janus Benchmark-C: Face dataset and protocol [Paper]
- IJB-B: IARPA Janus Benchmark-B Face Dataset [Paper]
- IJB-A: Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A [Paper]
- Unconstrained Face Detection and Open-Set Face Recognition Challenge [Project] [Paper]
- MegaFace: 1 Million Faces for Recognition at Scale, 690,572 subjects [Download]
Template Generators <a name="face-recognition-template-generator"></a>
Pretrained Models <a name="face-recognition-pre-trained-model"></a>
- ResNet-101, DenseNet-121 provided by FaRE
- ResNet-50, SE-ResNet-50 provided by VGG-Face2 [Download]
- VGG-16 provided by VGG-Face
- InsightFace [Download]
Image-based Template Genearator <a name="face-recognition-image-template-generator"></a>
- Pairwise Relation Network, ECCV18: [Paper]
- GridFace: Face Rectification via Learning Local Homography Transformation, ECCV18: [Paper]
- Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition, ECCV18: [Paper]
- Face Recognition with Contrastive Convolution, ECCV18: [Paper]
- FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR15 [Paper] [TensorFlow]
- DeepID series, CVPR14: [DeepID] [DeepID2] [DeepID3]
- DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR14: [Paper]
Image-set-based Template Generator <a name="face-recognition-set-template-generator"></a>
- Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets, ECCV, 2018
- Comparator Network, ECCV, 2018 [Pytorch]
Training Loss <a name="face-recognition-training-loss"></a>
- InsightFace (ArcFace): Additive Angular Margin Loss for Deep Face Recognition, ArXiv, 2018 [MXNet]
- CosFace: Large Margin Cosine Loss for Deep Face Recognition, CVPR, 2018 [TensorFlow] [MXNet]
- Ring loss: Convex Feature Normalization for Face Recognition [Paper] [PyTorch]
- Git Loss for Deep Face Recognition [Paper]
- A-Softmax Loss (SphereFace) [Paper] [Caffe] (Caffe)
- Triplet Loss [Paper] [Torch] [TensorFlow]
- Center Loss [Paper] [Caffe + MATLAB] [MXNet]
- Range Loss [Paper] [Caffe]
- L-Softmax [Paper] [Caffe] [MXNet]
- Marginal Loss [Paper]
Face Recognition Pipeline <a name="face-recognition-pipeline"></a>
- UR2D-E:Evaluation of a 3D-aided Pose Invariant 2D Face Recognition System
- SeetaFaceEngine: An open source C++ face recognition engine. [C++]
- OpenFace: Face recognition with Google's FaceNet deep neural network using Torch] [Torch +Python]
Face Genearation <a name="face-generation"></a>
Survey <a name="face-generation-survey"></a>
Datasets <a name="face-generation-datasets"></a>
Research <a name="face-generation-research"></a>
- TP-GAN: [Paper]
- FF-GAN: [Paper]
- DR-GAN: [Paper] [Website]
- BEGAN: Boundary Equilibrium Generative Adversarial Networks [Paper]