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FaceRecognition-Papers

Face Recognition related Papers<br>

2014

DeepFace: Closing the Gap to Human-Level Performance in Face Verification<br> https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf<br> [DeepID1]Deep Learning Face Representation from Predicting 10,000 Classes<br> https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Sun_Deep_Learning_Face_2014_CVPR_paper.pdf<br> [DeepID2]Deep Learning Face Representation by Joint Identification-Verification<br> https://arxiv.org/abs/1406.4773<br> [DeepID2+]Deeply learned face representations are sparse, selective, and robust<br> https://arxiv.org/abs/1412.1265<br>

2015

Deep Face Recognition<br> https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf<br> [DeepID3]DeepID3: Face Recognition with Very Deep Neural Networks<br> https://arxiv.org/abs/1502.00873<br> FaceNet: A Unified Embedding for Face Recognition and Clustering<br> https://arxiv.org/abs/1503.03832<br> A Light CNN for Deep Face Representation with Noisy Labels<br> https://arxiv.org/abs/1511.02683<br>

2016

Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-spectral Hallucination and Low-rank Embedding<br> https://arxiv.org/abs/1611.06638<br> Large-Margin Softmax Loss for Convolutional Neural Networks<br> https://arxiv.org/abs/1612.02295<br> Range Loss for Deep Face Recognition with Long-tail<br> https://arxiv.org/abs/1611.08976<br> [centerloss]A Discriminative Feature Learning Approach for Deep Face Recognition<br> https://ydwen.github.io/papers/WenECCV16.pdf<br>

2017

L2-constrained Softmax Loss for Discriminative Face Verification<br> https://arxiv.org/pdf/1703.09507.pdf<br> Marginal Loss for Deep Face Recognition<br> https://ibug.doc.ic.ac.uk/media/uploads/documents/deng_marginal_loss_for_cvpr_2017_paper.pdf<br> [cocoloss v1]Learning Deep Features via Congenerous Cosine Loss for Person Recognition<br> https://arxiv.org/abs/1702.06890<br> Learning Deep Features via Congenerous Cosine Loss for Person Recognition<br> https://arxiv.org/abs/1702.06890<br> SphereFace: Deep Hypersphere Embedding for Face Recognition<br> https://arxiv.org/abs/1704.08063<br> NormFace: L2 Hypersphere Embedding for Face Verification<br> https://arxiv.org/abs/1704.06369<br> [cocoloss v2] Rethinking Feature Discrimination and Polymerization for Large-scale Recognition<br> https://arxiv.org/abs/1710.00870<br>

2018

ArcFace: Additive Angular Margin Loss for Deep Face Recognition<br> https://arxiv.org/abs/1801.07698<br> Additive Margin Softmax for Face Verification<br> https://arxiv.org/abs/1801.05599<br> Ring loss: Convex Feature Normalization for Face Recognition<br> https://arxiv.org/abs/1803.00130<br> CosFace: Large Margin Cosine Loss for Deep Face Recognition<br> https://arxiv.org/abs/1801.09414<br> MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices<br> https://arxiv.org/abs/1804.07573<br> Deep Face Recognition: A Survey<br> https://arxiv.org/abs/1804.06655<br> Wildest Faces: Face Detection and Recognition in Violent Settings<br> https://arxiv.org/abs/1805.07566<br> Pose-Robust Face Recognition via Deep Residual Equivariant Mapping<br> https://arxiv.org/abs/1803.00839<br> Minimum Margin Loss for Deep Face Recognition<br> https://arxiv.org/abs/1805.06741<br> Fully Associative Patch-based 1-to-N Matcher for Face Recognition<br> https://arxiv.org/abs/1805.06306<br> Towards Interpretable Face Recognition<br> https://arxiv.org/abs/1805.00611<br> Robust Face Recognition with Deeply Normalized Depth Images<br> https://arxiv.org/abs/1805.00406<br>