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https://github.com/L706077/DNN-Face-Recognition-Papers

awesome deep learning papers for face recognition
https://github.com/L706077/DNN-Face-Recognition-Papers

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awesome deep learning papers for face recognition

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# DNN-Face-Recognition-Papers

awesome deep learning papers for face recognition



## DeepFace

- [DeepFace: Closing the Gap to Human-Level Performance in Face Verification](https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf) [Yaniv Taigman et al., 2014]



- [Web-Scale Training for Face Identification](https://arxiv.org/abs/1406.5266) [Yaniv Taigman et al., 2015]



## DeepID Series

- [Deep Learning Face Representation from Predicting 10,000 Classes](http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf) [Yi Sun et al., 2014]



- [Deep Learning Face Representation by Joint Identification-Verification](https://arxiv.org/abs/1406.4773) [Yi Sun et al., 2014]



- [Deeply learned face representations are sparse, selective, and robust](https://arxiv.org/abs/1412.1265) [Yi Sun et al., 2014]



- [DeepID3: Face Recognition with Very Deep Neural Networks](https://arxiv.org/abs/1502.00873) [Yi Sun et al., 2015]



## FaceNet

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Schroff_FaceNet_A_Unified_2015_CVPR_paper.html) [Florian Schroff et al., 2015]



## WebFace

- [Learning Face Representation from Scratch](https://arxiv.org/pdf/1411.7923.pdf) [Dong Yi et al., 2014]



- [A Lightened CNN for Deep Face Representation](https://pdfs.semanticscholar.org/d4e6/69d5d35fa0ca9f8d9a193c82d4153f5ffc4e.pdf) [[Xiang Wu et al., 2015]



- [A Light CNN for Deep Face Representation with Noisy Labels](https://arxiv.org/abs/1511.02683) [Xiang Wu et al., 2017]



## VGGFace

- [Deep Face Recognition](https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf) [Omkar M. Parkhi et al., 2015]



## Baidu Research

- [Targeting Ultimate Accuracy: Face Recognition via Deep Embedding](https://arxiv.org/abs/1506.07310) [Jingtuo Liu et al., 2015]



## Face++

- [Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?](https://arxiv.org/abs/1501.04690) [Erjin Zhou et al., 2015]



## OpenFace

- [OpenFace: A general-purpose face recognition library with mobile applications](https://cmusatyalab.github.io/openface/) [Brandon Amos et al., 2016]



## Pruning Network

- [DSD: Dense-Sparse-Dense Training for Deep Neural Networks](https://arxiv.org/abs/1607.04381) [Song Han et al., 2017]



- [Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning](https://arxiv.org/abs/1611.06440) [Pavlo Molchanov et al., 2017]



- [Learning both Weights and Connections for Efficient Neural Networks](https://arxiv.org/abs/1506.02626) [Song Han et al., 2016]



## Center Face (center loss)

- [A Discriminative Feature Learning Approach for Deep Face Recognition](http://ydwen.github.io/papers/WenECCV16.pdf) [Yandong Wen et al., 2016]



## Loss fuction

- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://arxiv.org/pdf/1704.01719.pdf) [Weihua Chen et al., 2017]



- [Range Loss for Deep Face Recognition with Long-tail](https://arxiv.org/abs/1611.08976) [Xiao Zhang et al., 2016]



## Joint Bayesian

- [Bayesian Face Revisited: A Joint Formulation](http://www.jiansun.org/papers/ECCV12_BayesianFace.pdf) [Dong Chen et al., 2012]



- [A Practical Transfer Learning Algorithm for Face Verification](http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Cao_A_Practical_Transfer_2013_ICCV_paper.pdf) [Xudong Cao et al., 2013]



## LFW

- [Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments](http://vis-www.cs.umass.edu/lfw/lfw.pdf) [Gary B. et al., 2012]



## MegaFace

- [MegaFace: A Million Faces for Recognition at Scale](https://arxiv.org/abs/1505.02108) [D. Miller et al., 2016]



- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Kemelmacher-Shlizerman_The_MegaFace_Benchmark_CVPR_2016_paper.pdf) [Ira Kemelmacher-Shlizerman et al., 2016]



## MS Celebrity

- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://www.microsoft.com/en-us/research/project/ms-celeb-1m-challenge-recognizing-one-million-celebrities-real-world/) [Yandong Guo et al., 2016]



## Feature Normalization/Loss Function Method

  - [Large-Margin Softmax Loss for Convolutional Neural Networks(L-Softmax loss)](https://arxiv.org/pdf/1612.02295.pdf) [Weiyang Liu al., 2017] [code](https://github.com/wy1iu/LargeMargin_Softmax_Loss)



- [SphereFace: Deep Hypersphere Embedding for Face Recognition(A-Softmax loss)](https://arxiv.org/abs/1704.08063) [Weiyang Liu al., 2017]



- [L2-constrained Softmax Loss for Discriminative Face Verification](https://arxiv.org/abs/1703.09507v2) [Rajeev Ranjan al., 2017]



- [Rethinking Feature Discrimination and Polymerization for Large-scale Recognition(CoCo loss)](https://arxiv.org/pdf/1710.00870.pdf) [Yu Liu al., 2017]



- [NormFace: L2 Hypersphere Embedding for Face Verification](https://arxiv.org/pdf/1704.06369.pdf) [Feng Wang al., 2017]



- [ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/pdf/1801.07698.pdf) [Jiankang Deng al., 2018]



- [DeepVisage: Making face recognition simple yet with powerful generalization skills](https://arxiv.org/abs/1703.08388) [Abul Hasnat al., 2017]



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- [reference 1 ](https://zhuanlan.zhihu.com/p/33288325)



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## Angular margin Series: 

- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://arxiv.org/abs/1704.08063) [Weiyang Liu al., 2017] [code](https://github.com/wy1iu/sphereface)



- [AM : Additive Margin Softmax for Face Verification](https://arxiv.org/pdf/1801.05599.pdf) [Feng Wang al., 2018] [code](https://github.com/happynear/AMSoftmax)



- [CCL : Face Recognition via Centralized Coordinate Learning](https://arxiv.org/pdf/1801.05678.pdf) [Xianbiao al., 2018]



- [CosFace: Large Margin Cosine Loss for Deep Face Recognition(Tencent AI Lab)](https://arxiv.org/pdf/1801.09414.pdf) [Hao Wang al., 2018]



- [AAM : ArcFace: ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/pdf/1801.07698.pdf) [Jiankang Deng al., 2018] [code](https://github.com/deepinsight/insightface)



- [MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices](https://arxiv.org/abs/1804.07573) [Sheng Chen al., 2018] [code](https://github.com/imistyrain/MobileFaceNet)



## 3D Face Recognition:

- [Deep 3D Face Identification](https://arxiv.org/pdf/1703.10714.pdf) [Donghyun Kim al., 2017]



- [Learning from Millions of 3D Scans for Large-scale 3D Face Recognition](https://arxiv.org/pdf/1711.05942.pdf) [S. Z. Gilani al.,2018]



## SenseTime

- [Exploring Disentangled Feature Representation Beyond Face Identification](https://arxiv.org/pdf/1804.03487.pdf) [Yu Liu al. ,2018]