DNN-Face-Recognition-Papers

awesome deep learning papers for face recognition

DeepFace

• DeepFace: Closing the Gap to Human-Level Performance in Face Verification [Yaniv Taigman et al., 2014]

• Web-Scale Training for Face Identification [Yaniv Taigman et al., 2015]

DeepID Series

• Deep Learning Face Representation from Predicting 10,000 Classes [Yi Sun et al., 2014]

• Deep Learning Face Representation by Joint Identification-Verification [Yi Sun et al., 2014]

• Deeply learned face representations are sparse, selective, and robust [Yi Sun et al., 2014]

• DeepID3: Face Recognition with Very Deep Neural Networks [Yi Sun et al., 2015]

FaceNet

• FaceNet: A Unified Embedding for Face Recognition and Clustering [Florian Schroff et al., 2015]

WebFace

• Learning Face Representation from Scratch [Dong Yi et al., 2014]

• A Lightened CNN for Deep Face Representation [[Xiang Wu et al., 2015]

• A Light CNN for Deep Face Representation with Noisy Labels [Xiang Wu et al., 2017]

VGGFace

• Deep Face Recognition [Omkar M. Parkhi et al., 2015]

Baidu Research

• Targeting Ultimate Accuracy: Face Recognition via Deep Embedding [Jingtuo Liu et al., 2015]

Face++

• Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not? [Erjin Zhou et al., 2015]

OpenFace

• OpenFace: A general-purpose face recognition library with mobile applications [Brandon Amos et al., 2016]

Pruning Network

• DSD: Dense-Sparse-Dense Training for Deep Neural Networks [Song Han et al., 2017]

• Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning [Pavlo Molchanov et al., 2017]

• Learning both Weights and Connections for Efficient Neural Networks [Song Han et al., 2016]

Center Face (center loss)

• A Discriminative Feature Learning Approach for Deep Face Recognition [Yandong Wen et al., 2016]

Loss fuction

• Beyond triplet loss: a deep quadruplet network for person re-identification [Weihua Chen et al., 2017]

• Range Loss for Deep Face Recognition with Long-tail [Xiao Zhang et al., 2016]

Joint Bayesian

• Bayesian Face Revisited: A Joint Formulation [Dong Chen et al., 2012]

• A Practical Transfer Learning Algorithm for Face Verification [Xudong Cao et al., 2013]

LFW

• Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments [Gary B. et al., 2012]

MegaFace

• MegaFace: A Million Faces for Recognition at Scale [D. Miller et al., 2016]

• The MegaFace Benchmark: 1 Million Faces for Recognition at Scale [Ira Kemelmacher-Shlizerman et al., 2016]

MS Celebrity

• MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition [Yandong Guo et al., 2016]

Feature Normalization/Loss Function Method

• Large-Margin Softmax Loss for Convolutional Neural Networks(L-Softmax loss) [Weiyang Liu al., 2017] code

• SphereFace: Deep Hypersphere Embedding for Face Recognition(A-Softmax loss) [Weiyang Liu al., 2017]

• L2-constrained Softmax Loss for Discriminative Face Verification [Rajeev Ranjan al., 2017]

• Rethinking Feature Discrimination and Polymerization for Large-scale Recognition(CoCo loss) [Yu Liu al., 2017]

• NormFace: L2 Hypersphere Embedding for Face Verification [Feng Wang al., 2017]

• ArcFace: Additive Angular Margin Loss for Deep Face Recognition [Jiankang Deng al., 2018]

• DeepVisage: Making face recognition simple yet with powerful generalization skills [Abul Hasnat al., 2017]

• reference 1

Angular margin Series:

• SphereFace: Deep Hypersphere Embedding for Face Recognition [Weiyang Liu al., 2017] code

• AM : Additive Margin Softmax for Face Verification [Feng Wang al., 2018] code

• CCL : Face Recognition via Centralized Coordinate Learning [Xianbiao al., 2018]

• CosFace: Large Margin Cosine Loss for Deep Face Recognition(Tencent AI Lab) [Hao Wang al., 2018]

• AAM : ArcFace: ArcFace: Additive Angular Margin Loss for Deep Face Recognition [Jiankang Deng al., 2018] code

• MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices [Sheng Chen al., 2018] code

3D Face Recognition:

• Deep 3D Face Identification [Donghyun Kim al., 2017]

• Learning from Millions of 3D Scans for Large-scale 3D Face Recognition [S. Z. Gilani al.,2018]

SenseTime

• Exploring Disentangled Feature Representation Beyond Face Identification [Yu Liu al. ,2018]