最近（2017/2018）人脸识别的相关论文
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).
来自知乎的图片
这个得从2016年说起，2016年有两篇文章（Center Loss和Large Margin Softmax）首次给出了softmax损失函数的可视化图像，人们才知道原来特征的分布是长这个样子的：
<img src="https://pic4.zhimg.com/50/v2-c68a088599b6afdbafac1cf7d80b5f0e_hd.jpg" data-caption="" data-size="normal" data-rawwidth="592" data-rawheight="468" class="origin_image zh-lightbox-thumb" width="592" data-original="https://pic4.zhimg.com/v2-c68a088599b6afdbafac1cf7d80b5f0e_r.jpg">
于是就开始了各种研究了，有把特征变细的：
<img src="https://pic3.zhimg.com/50/v2-b95aeda7b26ad880f363a9f18f1cf1e3_hd.jpg" data-caption="" data-size="normal" data-rawwidth="584" data-rawheight="549" class="origin_image zh-lightbox-thumb" width="584" data-original="https://pic3.zhimg.com/v2-b95aeda7b26ad880f363a9f18f1cf1e3_r.jpg">
有让特征向各自类中心收缩的：
<img src="https://pic1.zhimg.com/50/v2-4ec741a5c1b46c377bc1c822078dc0c5_hd.jpg" data-caption="" data-size="normal" data-rawwidth="618" data-rawheight="549" class="origin_image zh-lightbox-thumb" width="618" data-original="https://pic1.zhimg.com/v2-4ec741a5c1b46c377bc1c822078dc0c5_r.jpg">
有把特征投影到球上的：
<img src="https://pic3.zhimg.com/50/v2-34989e27af83440660a75750acaf9a74_hd.jpg" data-caption="" data-size="normal" data-rawwidth="506" data-rawheight="460" class="origin_image zh-lightbox-thumb" width="506" data-original="https://pic3.zhimg.com/v2-34989e27af83440660a75750acaf9a74_r.jpg">
总之把特征可视化之后，人们发现特征原来是呈这种放射型分布的，而放射型分布就可以从幅度和角度两个方向来尝试改进分类函数，于是2017年人们要么改改幅度，要么改改角度，水了一堆paper。