转自:https://blog.csdn.net/u011808673/article/details/80650335
Following is a growing list of some of the materials I found on the web for research on face recognition algorithm.
1. [DeepFace](https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf).A work from Facebook.
2. [FaceNet](http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_089.pdf).A work from Google.
3. [ One Millisecond Face Alignment with an Ensemble of Regression Trees](http://www.csc.kth.se/~vahidk/papers/KazemiCVPR14.pdf). Dlib implements the algorithm.
4. [DeepID](http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf)
5. [DeepID2]([1406.4773] Deep Learning Face Representation by Joint Identification-Verification)
6. [DeepID3](Face Recognition with Very Deep Neural Networks)
7. [Learning Face Representation from Scratch]([1411.7923] Learning Face Representation from Scratch)
8. [Face Search at Scale: 80 Million Gallery](80 Million Gallery)
9. [A Discriminative Feature Learning Approach for Deep Face Recognition](http://ydwen.github.io/papers/WenECCV16.pdf)
10. [NormFace: L2 Hypersphere Embedding for Face Verification](https://arxiv.org/abs/1704.06369).* attention: model released !*
11. [SphereFace: Deep Hypersphere Embedding for Face Recognition](Deep Hypersphere Embedding for Face Recognition)
12.[VGGFace2: A dataset for recognising faces across pose and age ]A dataset for recognising faces across pose and age
1. [CASIA WebFace Database](Center for Biometrics and Security Research). 10,575 subjects and 494,414 images
2. [Labeled Faces in the Wild](http://vis-www.cs.umass.edu/lfw/).13,000 images and 5749 subjects
3. [Large-scale CelebFaces Attributes (CelebA) Dataset](403 Forbidden) 202,599 images and 10,177 subjects. 5 landmark locations, 40 binary attributes.
4. [MSRA-CFW](MSRA-CFW: Data Set of Celebrity Faces on the Web - Microsoft Research). 202,792 images and 1,583 subjects.
5. [MegaFace Dataset](MegaFace) 1 Million Faces for Recognition at Scale
690,572 unique people
6. [FaceScrub](vintage - resources). A Dataset With Over 100,000 Face Images of 530 People.
7. [FDDB](FDDB : Main).Face Detection and Data Set Benchmark. 5k images.
8. [AFLW](ICG - Research).Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization. 25k images.
9. [AFW](Face Detection Matlab Code). Annotated Faces in the Wild. ~1k images.
10.[3D Mask Attack Dataset](3D Mask Attack Dataset). 76500 frames of 17 persons using Kinect RGBD with eye positions (Sebastien Marcel)
11. [Audio-visual database for face and speaker recognition](MOBIO - DDP).Mobile Biometry MOBIO http://www.mobioproject.org/
12. [BANCA face and voice database](The BANCA Database). Univ of Surrey
13. [Binghampton Univ 3D static and dynamic facial expression database](http://www.cs.binghamton.edu/~lijun/Research/3DFE/3DFE_Analysis.html). (Lijun Yin, Peter Gerhardstein and teammates)
14. [The BioID Face Database](BioID Face Database | Dataset for Face Detection | facedb - BioID). BioID group
15. [Biwi 3D Audiovisual Corpus of Affective Communication](ETHZ - Computer Vision Lab:). 1000 high quality, dynamic 3D scans of faces, recorded while pronouncing a set of English sentences.
16. [Cohn-Kanade AU-Coded Expression Database](The Affect Analysis Group at Pittsburgh). 500+ expression sequences of 100+ subjects, coded by activated Action Units (Affect Analysis Group, Univ. of Pittsburgh.
17. [CMU/MIT Frontal Faces ](CBCL SOFTWARE). Training set: 2,429 faces, 4,548 non-faces; Test set: 472 faces, 23,573 non-faces.
18. [AT&T Database of Faces](The Database of Faces) 400 faces of 40 people (10 images per people)
1. [openface](cmusatyalab/openface). Face recognition with Google's FaceNet deep neural network using Torch.
2. [VGG-Face](VGG Face Descriptor). VGG-Face CNN descriptor. Impressed embedding loss.
3. [SeetaFace Engine](seetaface/SeetaFaceEngine). SeetaFace Engine is an open source C++ face recognition engine, which can run on CPU with no third-party dependence.
4. [Caffe-face](ydwen/caffe-face) - Caffe Face is developed for face recognition using deep neural networks.
5. [Norm-Face](happynear/NormFace) - Norm Face, finetuned from [center-face](ydwen/caffe-face) and [Light-CNN](AlfredXiangWu/face_verification_experiment)
6. [VGG-Face2]VGG-Face 2Dataset
1. [OpenCV](OpenCV library). With some trained face detector models.
2. [dlib](dlib C++ Library - Machine Learning). Dlib implements a state-of-the-art of face Alignment algorithm.
3. [ccv](liuliu/ccv). With a state-of-the-art frontal face detector
4. [libfacedetection](ShiqiYu/libfacedetection). A binary library for face detection in images.
5. [SeetaFaceEngine](seetaface/SeetaFaceEngine). An open source C++ face recognition engine.
1. [Caffe](Caffe | Deep Learning Framework)
2. [Torch7](torch/torch7)
3. [Theano](Welcome - Theano 1.0.0 documentation)
4. [cuda-convnet](https://code.google.com/p/cuda-convnet/)
5. [MXNET](apache/incubator-mxnet)
6. [Tensorflow](tensorflow)
7. [tiny-dnn](tiny-dnn/tiny-dnn)
1. [faceswap](matthewearl/faceswap) Face swapping with Python, dlib, and OpenCV
2. [Facial Keypoints Detection](Facial Keypoints Detection | Kaggle) Competition on Kaggle.
3. [An implementation of Face Alignment at 3000fps via Local Binary Features](freesouls/face-alignment-at-3000fps)
layout: post
category: deep_learning
title: Face Recognition
Deep Learning Face Representation from Predicting 10,000 Classes
Deep Learning Face Representation by Joint Identification-Verification
基于Caffe的DeepID2实现
Deeply learned face representations are sparse, selective, and robust
MobileID: Face Model Compression by Distilling Knowledge from Neurons
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Deep Face Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
Real time face detection and recognition
Targeting Ultimate Accuracy: Face Recognition via Deep Embedding
Learning Robust Deep Face Representation
A Light CNN for Deep Face Representation with Noisy Labels
Pose-Aware Face Recognition in the Wild
Triplet Probabilistic Embedding for Face Verification and Clustering
Recurrent Regression for Face Recognition
A Discriminative Feature Learning Approach for Deep Face Recognition
Deep Face Recognition with Center Invariant Loss
How Image Degradations Affect Deep CNN-based Face Recognition?
VIPLFaceNet: An Open Source Deep Face Recognition SDK
SeetaFace Engine
A Discriminative Feature Learning Approach for Deep Face Recognition
Sparsifying Neural Network Connections for Face Recognition
Range Loss for Deep Face Recognition with Long-tail
Hybrid Deep Learning for Face Verification
Towards End-to-End Face Recognition through Alignment Learning
Multi-Task Convolutional Neural Network for Face Recognition
NormFace: L2 Hypersphere Embedding for Face Verification
SphereFace: Deep Hypersphere Embedding for Face Recognition
L2-constrained Softmax Loss for Discriminative Face Verification
https://arxiv.org/abs/1703.09507
Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture
Enhancing Convolutional Neural Networks for Face Recognition with Occlusion Maps and Batch Triplet Loss
https://arxiv.org/abs/1707.07923
Model Distillation with Knowledge Transfer in Face Classification, Alignment and Verification
https://arxiv.org/abs/1709.02929
Improving Heterogeneous Face Recognition with Conditional Adversarial Networks
https://arxiv.org/abs/1709.02848
Face Sketch Matching via Coupled Deep Transform Learning
Additive Margin Softmax for Face Verification
Face Recognition via Centralized Coordinate Learning
https://arxiv.org/abs/1801.05678
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
CosFace: Large Margin Cosine Loss for Deep Face Recognition
https://arxiv.org/abs/1801.09414
Ring loss: Convex Feature Normalization for Face Recognition
Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
Attention-Set based Metric Learning for Video Face Recognition
https://arxiv.org/abs/1704.03805
SeqFace: Make full use of sequence information for face recognitio
Deep Convolutional Network Cascade for Facial Point Detection
Facial Landmark Detection by Deep Multi-task Learning
A Recurrent Encoder-Decoder Network for Sequential Face Alignment
RED-Net: A Recurrent Encoder-Decoder Network for Video-based Face Alignment
Detecting facial landmarks in the video based on a hybrid framework
Deep Constrained Local Models for Facial Landmark Detection
Effective face landmark localization via single deep network
A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection
https://arxiv.org/abs/1704.01880
Deep Alignment Network: A convolutional neural network for robust face alignment
Joint Multi-view Face Alignment in the Wild
https://arxiv.org/abs/1708.06023
FacePoseNet: Making a Case for Landmark-Free Face Alignment
https://arxiv.org/abs/1708.07517
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
https://arxiv.org/abs/1711.06753
Brute-Force Facial Landmark Analysis With A 140,000-Way Classifier
Style Aggregated Network for Facial Landmark Detection
Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment
https://arxiv.org/abs/1803.05588
Using MXNet for Face-related Algorithm
clmtrackr: Javascript library for precise tracking of facial features via Constrained Local Models
DeepLogo
Deep-Leafsnap
FaceVerification: An Experimental Implementation of Face Verification, 96.8% on LFW
InsightFace
OpenFace: Face Recognition with Deep Neural Networks
OpenFace 0.2.0: Higher accuracy and halved execution time
OpenFace: A general-purpose face recognition library with mobile applications
OpenFace: an open source facial behavior analysis toolkit
Face-Resources