最近在做人脸识别相关的工作, 比较关注这方面的学术动态. 发现对人脸识别的Loss函数改进的论文比较多, 如:
[2017] L2-constrained Softmax Loss for Discriminative Face Verification
[2017 ACM MM] NormFace_ L2 Hypersphere Embedding for Face Verification
[2017 CVPR] SphereFace_ Deep Hypersphere Embedding for Face Recognition (A_Softmax Loss)
[2017 NIPS] Rethinking Feature Discrimination and Polymerization for Large-scale Recognition (COCO Loss)
[2017 ICCV] Deep Metric Learning with Angular Loss
[2017] Contrastive-center loss for deep neural networks
[2017 CVPR] Range Loss for Deep Face Recognition with Long-tail
2018年伊始也出几两篇相关改进的论文:
[2018] Additive Margin Softmax for Face Verification
[2018] Face Recognition via Centralized Coordinate Learning
[2018] ArcFace_ Additive Angular Margin Loss for Deep Face Recognition
人脸识别还有一些其他难点和热点的, 比如
1) 基于视频的人脸识别
[2017 CVPR] Neural Aggregation Network for Video Face Recognition;
[2017 PAMI] Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition
2) 三维人脸识别
[2017] Deep 3D Face Identification
[2017] Learning from Millions of 3D Scans for Large-scale 3D Face Recognition
3) 跨年龄的人脸识别
[2017 PRL] Large Age-Gap face verification by feature injection in deep networks
[2017] Cross-Age LFW_ A Database for Studying Cross-Age Face Recognition in Unconstrained Environments
4) 少样本人脸识别
[2017] One-shot Face Recognition by Promoting Underrepresented Classes;
[2017] SSPP-DAN_ Deep Domain Adaptation Network for Face Recognition with Single Sample Per Person
5) 遮挡情况下的人脸识别
[2017 ICCVW] Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network
[2017] Enhancing Convolutional Neural Networks for Face Recognition with Occlusion Maps and Batch Triplet Loss
6) 多模型特征融合
[2017 PAMI] Face Search at Scale
[2017] Deep Heterogeneous Feature Fusion for Template-Based Face Recognition
还有注意到现在人脸识别的评价方式逐渐转向更贴近实用的1:N的开集测试([2017 CVPRW] Toward Open Set Face Recognition).
