Face Related Papers and Code Collection

Any face research/engineer related merge request is wellcome! 02/08/2019.

Index

1. Toolkits
2. Face Detection
   • Survey
   • Datasets
   • Research
3. Face Alignment
   • Survey
   • Datasets
   • Research
4. Face Recosntruction
   • Survey
   • Datasets
   • Research
5. Face Recognition
   • Survey
   • Tutorial
   • Datasets
   • Template Generator
     • Pretrained models
     • Image-based Template Generator
     • Image-set-based Template Generator
   • Face Recognition Pipeline
6. Face Generation
   • Survey
   • Datasets
   • Research
7. Face Attributes Analysis
   • Survey
   • Datasets
   • Research

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]
• S³FD: 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>

• Deep Learning for Face Recognition

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 Recogn