Rectified Wing Loss for Efficient and Robust Facial Landmark Localisation with Convolutional Neural Networks
出版年份 2019 全文链接
标题
Rectified Wing Loss for Efficient and Robust Facial Landmark Localisation with Convolutional Neural Networks
作者
关键词
-
出版物
INTERNATIONAL JOURNAL OF COMPUTER VISION
Volume -, Issue -, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2019-12-17
DOI
10.1007/s11263-019-01275-0
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Mining Hard Augmented Samples for Robust Facial Landmark Localization With CNNs
- (2019) Zhen-Hua Feng et al. IEEE SIGNAL PROCESSING LETTERS
- Joint Multi-View Face Alignment in the Wild
- (2019) Jiankang Deng et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Deep Context-Sensitive Facial Landmark Detection With Tree-Structured Modeling
- (2018) Jiajian Zeng et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Towards Robust and Accurate Multi-View and Partially-Occluded Face Alignment
- (2018) Junliang Xing et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Recurrent Shape Regression
- (2018) Zhen Cui et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- GoDP: Globally Optimized Dual Pathway deep network architecture for facial landmark localization in-the-wild
- (2018) Yuhang Wu et al. IMAGE AND VISION COMPUTING
- Facial Landmark Detection: A Literature Survey
- (2018) Yue Wu et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Facial feature point detection: A comprehensive survey
- (2018) Nannan Wang et al. NEUROCOMPUTING
- Gaussian mixture 3D morphable face model
- (2018) Paul Koppen et al. PATTERN RECOGNITION
- Facial Landmark Detection with Tweaked Convolutional Neural Networks
- (2017) Yue Wu et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Pose-Invariant Face Alignment via CNN-Based Dense 3D Model Fitting
- (2017) Amin Jourabloo et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
- (2016) Kaipeng Zhang et al. IEEE SIGNAL PROCESSING LETTERS
- Face Alignment via Regressing Local Binary Features
- (2016) Shaoqing Ren et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Learning Cascaded Deep Auto-Encoder Networks for Face Alignment
- (2016) Renliang Weng et al. IEEE TRANSACTIONS ON MULTIMEDIA
- Multi-view facial landmark detector learned by the Structured Output SVM
- (2016) Michal Uřičář et al. IMAGE AND VISION COMPUTING
- 300 Faces In-The-Wild Challenge: database and results
- (2016) Christos Sagonas et al. IMAGE AND VISION COMPUTING
- L2,1-based regression and prediction accumulation across views for robust facial landmark detection
- (2016) Brais Martinez et al. IMAGE AND VISION COMPUTING
- Multi-view facial landmark detection by using a 3D shape model
- (2016) Jan Čech et al. IMAGE AND VISION COMPUTING
- Approaching human level facial landmark localization by deep learning
- (2016) Haoqiang Fan et al. IMAGE AND VISION COMPUTING
- M3 CSR: Multi-view, multi-scale and multi-component cascade shape regression
- (2016) Jiankang Deng et al. IMAGE AND VISION COMPUTING
- Adaptive Cascade Deep Convolutional Neural Networks for face alignment
- (2015) Yuan Dong et al. COMPUTER STANDARDS & INTERFACES
- Random Cascaded-Regression Copse for Robust Facial Landmark Detection
- (2015) Zhen-Hua Feng et al. IEEE SIGNAL PROCESSING LETTERS
- Cascaded Collaborative Regression for Robust Facial Landmark Detection Trained Using a Mixture of Synthetic and Real Images With Dynamic Weighting
- (2015) Zhen-Hua Feng et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Face Alignment by Explicit Shape Regression
- (2013) Xudong Cao et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- A Review of Active Appearance Models
- (2010) Xinbo Gao et al. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND RE
- A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
- (2008) Zhihong Zeng et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now