人脸论文集选

人脸论文集选

一、Face Detection

1. 级联网络用于人脸检测: A Convolutional Neural Network Cascade for Face Detection. CVPR2015
   code: https://github.com/anson0910/CNN_face_detection

2. 利用GAN检测Tiny Faces: Finding Tiny Faces in the Wild with Generative Adversarial Network. CVPR2018

3. 多尺度检测Tiny Faces: Finding Tiny Faces. CMU project. CVPR2017.
   code: https://github.com/peiyunh/tiny

4. MTCNN: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks. SPL, August, 2016
   code: https://github.com/kpzhang93/MTCNN_face_detection_alignment

二、 Face Recognition

1. 人证比对：DocFace+: ID Document to Selfie Matching. Arxiv, Sep, 2018
   code: https://github.com/seasonSH/DocFace

2. 大规模无标注数据标签传播方法: Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition. ECCV2018

3. 提出Range Loss解决Long-Tailed问题: Range Loss for Deep Face Recognition with Long-Tailed Training Data. ICCV2017

三、Face Attribute Learning

1. 部分共享的Multi-task学习: Partially Shared Multi-Task Convolutional Neural Network with Local Constraint for Face Attribute Learning. CVPR2018

2. 可解释的embedding空间: Context Embedding Networks. Arxiv, Sep 2017

四、Age Estimation

1. 利用Age Difference辅助Age Estimation: Facial Age Estimation With Age Difference. TIP, July, 2017

2. 利用随机森林进行Age Estimation: Deep Regression Forests for Age Estimation. Arxiv, Dec, 2017

3. 提出Mean-variance Loss: Mean-Variance Loss for Deep Age Estimation from a Face. CVPR2018

4. 跨性别/种族的Age Estimation: Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation. CVPR2018

5. 利用Anchored Regression Network进行Age Estimation和放大图像分辨率: Anchored Regression Networks applied to Age Estimation and Super Resolution. ICCV2017

6. 分级预测Age: Using Ranking-CNN for Age Estimation. CVPR2017

7. 利用BIF特征辅助预测，采用网络融合: Fusion Network for Face-based Age Estimation. Arxiv, Jul, 2018

8. Age Estimation网络的压缩: SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation. IJCAI2018
   code: https://github.com/shamangary/SSR-Net

9. Expression和Age之间的关系: Expression-Invariant Age Estimation Using Structured Learning. TPAMI, Feb, 2018

五、Age Progression

1. 利用金字塔结构的GAN生成图片: Learning Face Age Progression: A Pyramid Architecture of GANs. CVPR2018

2. 同样的CyleGAN: Generative Adversarial Style Transfer Networks for Face Aging. CVPR2018

六、Expression Recognition

1. 表情识别综述: Deep Facial Expression Recognition: A Survey. Arxiv, Apr, 2018

2. 联合pose一起进行表情识别: Joint Pose and Expression Modeling for Facial Expression Recognition. CVPR2018

3. 利用GAN提取各种表情与自然表情的差异信息辅助识别: Facial Expression Recognition by De-expression Residue Learning. CVPR2018

4. 提出Emotionet数据集，并结合Action Unit识别: EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild. CVPR2016

5. 表情强度预测: Deep Structured Learning for Facial Action Unit Intensity Estimation. CVPR2017

6. 两阶段的深度信息表情识别: FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition. FG, June, 2017

7. 挖掘temporal信息辅助识别: Facial Expression Recognition by De-expression Residue Learning. CVPR2018

七、Image Translation

1. 基于Landmark的微笑生成: Every Smile is Unique: Landmark-guided Diverse Smile Generation.
   CVPR2018

2. GANimation, 连续表情生成: GANimation: Anatomically-aware Facial Animation from a Single Image. ECCV2018
   code: https://github.com/albertpumarola/GANimation

3. Glow, 不同于GAN的可逆生成模型: Glow: Generative Flow with Invertible 1x1 Convolutions. OpenAI, Arxiv, Jul, 2018
   code: https://github.com/openai/glow

4. ModularGAN, 生成过程模块化: Modular Generative Adversarial Networks. Tencent, ECCV2018

5. StarGAN, 多领域风格图像生成: StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. CVPR2018
   code: https://github.com/yunjey/StarGAN

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