└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Face-Resources 2 | Following is a growing list of some of the materials I found on the web for research on face recognition algorithm. 3 | 4 | ## Papers 5 | 6 | 1. [DeepFace](https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf).A work from Facebook. 7 | 2. [FaceNet](http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_089.pdf).A work from Google. 8 | 3. [ One Millisecond Face Alignment with an Ensemble of Regression Trees](http://www.csc.kth.se/~vahidk/papers/KazemiCVPR14.pdf). Dlib implements the algorithm. 9 | 4. [DeepID](http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf) 10 | 5. [DeepID2](http://arxiv.org/abs/1406.4773) 11 | 6. [DeepID3](http://arxiv.org/abs/1502.00873) 12 | 7. [Learning Face Representation from Scratch](http://arxiv.org/abs/1411.7923) 13 | 8. [Face Search at Scale: 80 Million Gallery](http://arxiv.org/abs/1507.07242) 14 | 9. [A Discriminative Feature Learning Approach for Deep Face Recognition](http://ydwen.github.io/papers/WenECCV16.pdf) 15 | 16 | 10. [NormFace: L2 Hypersphere Embedding for Face Verification](https://arxiv.org/abs/1704.06369).* attention: model released !* 17 | 11. [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://arxiv.org/abs/1704.08063) 18 | 19 | ## Datasets 20 | 21 | 1. [CASIA WebFace Database](http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html). 10,575 subjects and 494,414 images 22 | 2. [Labeled Faces in the Wild](http://vis-www.cs.umass.edu/lfw/).13,000 images and 5749 subjects 23 | 3. [Large-scale CelebFaces Attributes (CelebA) Dataset](http://mmlab.ie.cuhk.edu.hk/projects/) 202,599 images and 10,177 subjects. 5 landmark locations, 40 binary attributes. 24 | 4. [MSRA-CFW](http://research.microsoft.com/en-us/projects/msra-cfw/). 202,792 images and 1,583 subjects. 25 | 5. [MegaFace Dataset](http://megaface.cs.washington.edu/) 1 Million Faces for Recognition at Scale 26 | 690,572 unique people 27 | 6. [FaceScrub](http://vintage.winklerbros.net/facescrub.html). A Dataset With Over 100,000 Face Images of 530 People. 28 | 7. [FDDB](http://vis-www.cs.umass.edu/fddb/).Face Detection and Data Set Benchmark. 5k images. 29 | 8. [AFLW](https://lrs.icg.tugraz.at/research/aflw/).Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization. 25k images. 30 | 9. [AFW](http://www.ics.uci.edu/~xzhu/face/). Annotated Faces in the Wild. ~1k images. 31 | 10.[3D Mask Attack Dataset](https://www.idiap.ch/dataset/3dmad). 76500 frames of 17 persons using Kinect RGBD with eye positions (Sebastien Marcel) 32 | 11. [Audio-visual database for face and speaker recognition](https://www.idiap.ch/dataset/mobio).Mobile Biometry MOBIO http://www.mobioproject.org/ 33 | 12. [BANCA face and voice database](http://www.ee.surrey.ac.uk/CVSSP/banca/). Univ of Surrey 34 | 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) 35 | 14. [The BioID Face Database](https://www.bioid.com/About/BioID-Face-Database). BioID group 36 | 15. [Biwi 3D Audiovisual Corpus of Affective Communication](http://www.vision.ee.ethz.ch/datasets/b3dac2.en.html). 1000 high quality, dynamic 3D scans of faces, recorded while pronouncing a set of English sentences. 37 | 16. [Cohn-Kanade AU-Coded Expression Database](http://www.pitt.edu/~emotion/ck-spread.htm). 500+ expression sequences of 100+ subjects, coded by activated Action Units (Affect Analysis Group, Univ. of Pittsburgh. 38 | 17. [CMU/MIT Frontal Faces ](http://cbcl.mit.edu/software-datasets/FaceData2.html). Training set: 2,429 faces, 4,548 non-faces; Test set: 472 faces, 23,573 non-faces. 39 | 18. [AT&T Database of Faces](http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html) 400 faces of 40 people (10 images per people) 40 | 41 | 42 | 43 | ## Trained Model 44 | 45 | 1. [openface](https://github.com/cmusatyalab/openface). Face recognition with Google's FaceNet deep neural network using Torch. 46 | 2. [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/). VGG-Face CNN descriptor. Impressed embedding loss. 47 | 3. [SeetaFace Engine](https://github.com/seetaface/SeetaFaceEngine). SeetaFace Engine is an open source C++ face recognition engine, which can run on CPU with no third-party dependence. 48 | 4. [Caffe-face](https://github.com/ydwen/caffe-face) - Caffe Face is developed for face recognition using deep neural networks. 49 | 50 | 5. [Norm-Face](https://github.com/happynear/NormFace) - Norm Face, finetuned from [center-face](https://github.com/ydwen/caffe-face) and [Light-CNN](https://github.com/AlfredXiangWu/face_verification_experiment) 51 | 52 | 53 | ## Tutorial 54 | 55 | 1. [Deep Learning for Face Recognition](http://valse.mmcheng.net/deep-learning-for-face-recognition/). Shiguan Shan, Xiaogang Wang, and Ming yang. 56 | 57 | ## Software 58 | 59 | 1. [OpenCV](http://opencv.org/). With some trained face detector models. 60 | 2. [dlib](http://dlib.net/ml.html). Dlib implements a state-of-the-art of face Alignment algorithm. 61 | 3. [ccv](https://github.com/liuliu/ccv). With a state-of-the-art frontal face detector 62 | 4. [libfacedetection](https://github.com/ShiqiYu/libfacedetection). A binary library for face detection in images. 63 | 5. [SeetaFaceEngine](https://github.com/seetaface/SeetaFaceEngine). An open source C++ face recognition engine. 64 | 65 | ##Frameworks 66 | 67 | 1. [Caffe](http://caffe.berkeleyvision.org/) 68 | 2. [Torch7](https://github.com/torch/torch7) 69 | 3. [Theano](http://deeplearning.net/software/theano/) 70 | 4. [cuda-convnet](https://code.google.com/p/cuda-convnet/) 71 | 5. [MXNET](https://github.com/dmlc/mxnet/) 72 | 6. [Tensorflow](https://github.com/tensorflow) 73 | 7. [tiny-dnn](https://github.com/tiny-dnn/tiny-dnn) 74 | 75 | ## Miscellaneous 76 | 77 | 1. [faceswap](https://github.com/matthewearl/faceswap) Face swapping with Python, dlib, and OpenCV 78 | 2. [Facial Keypoints Detection](https://www.kaggle.com/c/facial-keypoints-detection/details/deep-learning-tutorial) Competition on Kaggle. 79 | 3. [An implementation of Face Alignment at 3000fps via Local Binary Features](https://github.com/freesouls/face-alignment-at-3000fps) 80 | 81 | --- 82 | 83 | >Created by betars on 27/10/2015. 84 | --------------------------------------------------------------------------------