人臉識別[一] 演算法和資料庫總結

人臉識別[一] 演算法和資料庫總結

來自專欄人臉識別7 人贊了文章

Face-Resources

Following is a growing list of some of the materials I found on the web for research on face recognition algorithm.

Papers

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

Datasets

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)

Trained Model

1. [openface](cmusatyalab/openface). Face recognition with Googles 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

Software

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.

Frameworks

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)

Miscellaneous

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

date: 2015-10-09

Papers

DeepID

Deep Learning Face Representation from Predicting 10,000 Classes

• intro: CVPR 2014
• paper: http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf
• github: https://github.com/stdcoutzyx/DeepID_FaceClassify

DeepID2

Deep Learning Face Representation by Joint Identification-Verification

• paper: http://papers.nips.cc/paper/5416-analog-memories-in-a-balanced-rate-based-network-of-e-i-neurons

基於Caffe的DeepID2實現

• 1. http://www.miaoerduo.com/deep-learning/%E5%9F%BA%E4%BA%8Ecaffe%E7%9A%84deepid2%E5%AE%9E%E7%8E%B0%EF%BC%88%E4%B8%8A%EF%BC%89.html
• 2. http://www.miaoerduo.com/deep-learning/%E5%9F%BA%E4%BA%8Ecaffe%E7%9A%84deepid2%E5%AE%9E%E7%8E%B0%EF%BC%88%E4%B8%AD%EF%BC%89.html
• 3. http://www.miaoerduo.com/deep-learning/%E5%9F%BA%E4%BA%8Ecaffe%E7%9A%84deepid2%E5%AE%9E%E7%8E%B0%EF%BC%88%E4%B8%8B%EF%BC%89.html

DeepID2+

Deeply learned face representations are sparse, selective, and robust

• arxiv: http://arxiv.org/abs/1412.1265
• video: http://research.microsoft.com/apps/video/?id=260023
• mirror: http://pan.baidu.com/s/1boufl3x

MobileID

MobileID: Face Model Compression by Distilling Knowledge from Neurons

• intro: AAAI 2016 Oral. CUHK
• intro: MobileID is an extremely fast face recognition system by distilling knowledge from DeepID2
• project page: http://personal.ie.cuhk.edu.hk/~lz013/projects/MobileID.html
• paper: http://personal.ie.cuhk.edu.hk/~pluo/pdf/aaai16-face-model-compression.pdf
• github: https://github.com/liuziwei7/mobile-id

DeepFace

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

• intro: CVPR 2014. Facebook AI Research
• paper: https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf
• slides: http://valse.mmcheng.net/ftp/20141126/MingYang.pdf
• github: https://github.com/RiweiChen/DeepFace

Deep Face Recognition

• intro: BMVC 2015
• paper: http://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf
• homepage: http://www.robots.ox.ac.uk/~vgg/software/vgg_face/
• github(Keras): https://github.com/rcmalli/keras-vggface

FaceNet

FaceNet: A Unified Embedding for Face Recognition and Clustering

• intro: Google Inc. CVPR 2015
• arxiv: http://arxiv.org/abs/1503.03832
• github(Tensorflow): https://github.com/davidsandberg/facenet
• github(Caffe): https://github.com/hizhangp/triplet

Real time face detection and recognition

• intro: Real time face detection and recognition base on opencv/tensorflow/mtcnn/facenet
• github: https://github.com/shanren7/real_time_face_recognition

---

Targeting Ultimate Accuracy: Face Recognition via Deep Embedding

• intro: CVPR 2015
• arxiv: http://arxiv.org/abs/1506.07310

Learning Robust Deep Face Representation

• arxiv: https://arxiv.org/abs/1507.04844

A Light CNN for Deep Face Representation with Noisy Labels

• arxiv: https://arxiv.org/abs/1511.02683
• github: https://github.com/AlfredXiangWu/face_verification_experiment

Pose-Aware Face Recognition in the Wild

• paper: www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Masi_Pose-Aware_Face_Recognition_CVPR_2016_paper.pdf

Triplet Probabilistic Embedding for Face Verification and Clustering

• intro: Oral Paper in BTAS 2016; NVIDIA Best paper Award
• arxiv: https://arxiv.org/abs/1604.05417
• github(Keras): https://github.com/meownoid/face-identification-tpe

Recurrent Regression for Face Recognition

• arxiv: http://arxiv.org/abs/1607.06999

A Discriminative Feature Learning Approach for Deep Face Recognition

• intro: ECCV 2016
• intro: center loss
• paper: http://ydwen.github.io/papers/WenECCV16.pdf
• github: https://github.com/ydwen/caffe-face
• github: https://github.com/pangyupo/mxnet_center_loss

Deep Face Recognition with Center Invariant Loss

• intro: ACM MM Workshop
• paper: http://www1.ece.neu.edu/~yuewu/files/2017/twu024.pdf

How Image Degradations Affect Deep CNN-based Face Recognition?

• arxiv: http://arxiv.org/abs/1608.05246

VIPLFaceNet: An Open Source Deep Face Recognition SDK

• keywords: VIPLFaceNet / SeetaFace Engine
• arxiv: http://arxiv.org/abs/1609.03892

SeetaFace Engine

• intro: SeetaFace Engine is an open source C++ face recognition engine, which can run on CPU with no third-party dependence.
• github: https://github.com/seetaface/SeetaFaceEngine

A Discriminative Feature Learning Approach for Deep Face Recognition

• intro: ECCV 2016
• paper: http://ydwen.github.io/papers/WenECCV16.pdf

Sparsifying Neural Network Connections for Face Recognition

• paper: http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTcvpr16.pdf

Range Loss for Deep Face Recognition with Long-tail

• arxiv: https://arxiv.org/abs/1611.08976

Hybrid Deep Learning for Face Verification

• intro: TPAMI 2016. CNN+RBM
• paper: http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTpami16.pdf

Towards End-to-End Face Recognition through Alignment Learning

• intro: Tsinghua University
• arxiv: https://arxiv.org/abs/1701.07174

Multi-Task Convolutional Neural Network for Face Recognition

• arxiv: https://arxiv.org/abs/1702.04710

NormFace: L2 Hypersphere Embedding for Face Verification

• arxiv: https://arxiv.org/abs/1704.06369
• github: https://github.com/happynear/NormFace

SphereFace: Deep Hypersphere Embedding for Face Recognition

• intro: CVPR 2017
• arxiv: http://wyliu.com/papers/LiuCVPR17.pdf
• github: https://github.com/wy1iu/sphereface
• demo: http://v-wb.youku.com/v_show/id_XMjk3NTc1NjMxMg==.html

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

• intro: Amirkabir University of Technology & MIT
• arxiv: https://arxiv.org/abs/1706.06247

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

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1710.02914

Additive Margin Softmax for Face Verification

• keywords: additive margin Softmax (AM-Softmax),
• arxiv: https://arxiv.org/abs/1801.05599
• github: https://github.com/happynear/AMSoftmax

Face Recognition via Centralized Coordinate Learning

https://arxiv.org/abs/1801.05678

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

• arxiv: https://arxiv.org/abs/1801.07698
• github: https://github.com/deepinsight/insightface

CosFace: Large Margin Cosine Loss for Deep Face Recognition

https://arxiv.org/abs/1801.09414

Ring loss: Convex Feature Normalization for Face Recognition

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1803.00130

Pose-Robust Face Recognition via Deep Residual Equivariant Mapping

• intro: CVPR 2018. CUHK & SenseTime Research
• arxiv: https://arxiv.org/abs/1803.00839

Video Face Recognition

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

• arxiv: https://arxiv.org/abs/1803.06524
• github: https://github.com/huangyangyu/SeqFace

Facial Point / Landmark Detection

Deep Convolutional Network Cascade for Facial Point Detection

• homepage: http://mmlab.ie.cuhk.edu.hk/archive/CNN_FacePoint.htm
• paper: http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTcvpr13.pdf
• github: https://github.com/luoyetx/deep-landmark

Facial Landmark Detection by Deep Multi-task Learning

• intro: ECCV 2014
• project page: http://mmlab.ie.cuhk.edu.hk/projects/TCDCN.html
• paper: http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepfacealign.pdf
• github(Matlab): https://github.com/zhzhanp/TCDCN-face-alignment

A Recurrent Encoder-Decoder Network for Sequential Face Alignment

• intro: ECCV 2016 oral
• project page: https://sites.google.com/site/xipengcshomepage/eccv2016
• arxiv: https://arxiv.org/abs/1608.05477
• slides: https://drive.google.com/file/d/0B-FLp_bljv_1OTVrMF9OM21IbW8/view
• github: https://github.com/xipeng13/recurrent-face-alignment

RED-Net: A Recurrent Encoder-Decoder Network for Video-based Face Alignment

• intro: IJCV
• arxiv: https://arxiv.org/abs/1801.06066

Detecting facial landmarks in the video based on a hybrid framework

• arxiv: http://arxiv.org/abs/1609.06441

Deep Constrained Local Models for Facial Landmark Detection

• arxiv: https://arxiv.org/abs/1611.08657

Effective face landmark localization via single deep network

• arxiv: https://arxiv.org/abs/1702.02719

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

• intro: CVPRW 2017
• arxiv: https://arxiv.org/abs/1706.01789
• gihtub: https://github.com/MarekKowalski/DeepAlignmentNetwork

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

• intro: AAAI 2018
• arxiv: https://arxiv.org/abs/1802.01777
• github: https://github.com/mtli/BFFL

Style Aggregated Network for Facial Landmark Detection

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1803.04108
• github: https://github.com/D-X-Y/SAN

Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment

https://arxiv.org/abs/1803.05588

Projects

Using MXNet for Face-related Algorithm

• github: https://github.com/tornadomeet/mxnet-face

clmtrackr: Javascript library for precise tracking of facial features via Constrained Local Models

• github: https://github.com/auduno/clmtrackr
• blog: http://auduno.com/post/61888277175/fitting-faces
• demo: http://auduno.github.io/clmtrackr/examples/facesubstitution.html
• demo: http://auduno.github.io/clmtrackr/face_deformation_video.html
• demo: http://auduno.github.io/clmtrackr/examples/clm_emotiondetection.html
• demo: http://auduno.com/post/84214587523/twisting-faces

DeepLogo

• intro: A brand logo recognition system using deep convolutional neural networks.
• github: https://github.com/satojkovic/DeepLogo

Deep-Leafsnap

• intro: LeafSnap replicated using deep neural networks to test accuracy compared to traditional computer vision methods.
• github: https://github.com/sujithv28/Deep-Leafsnap

FaceVerification: An Experimental Implementation of Face Verification, 96.8% on LFW

• github: https://github.com/happynear/FaceVerification

InsightFace

• intro: Face Recognition Project on MXnet
• arxiv: https://github.com//deepinsight/insightface

OpenFace

OpenFace: Face Recognition with Deep Neural Networks

• homepage: http://cmusatyalab.github.io/openface/
• github: https://github.com/cmusatyalab/openface
• github: https://github.com/aybassiouny/OpenFaceCpp

OpenFace 0.2.0: Higher accuracy and halved execution time

• homepage: http://bamos.github.io/2016/01/19/openface-0.2.0/

OpenFace: A general-purpose face recognition library with mobile applications

• paper: http://reports-archive.adm.cs.cmu.edu/anon/anon/usr0/ftp/2016/CMU-CS-16-118.pdf

OpenFace: an open source facial behavior analysis toolkit

• intro: a state-of-the art open source tool intended for facial landmark detection, head pose estimation,
  facial action unit recognition, and eye-gaze estimation.
• github: https://github.com/TadasBaltrusaitis/OpenFace

Resources

Face-Resources

• github: https://github.com/betars/Face-Resources

---

Created by ruyiwei on 10/04/2018.

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