.png){kind=icon}
返回
Face alignment recurrent network
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2017.09.028.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a new facial landmark detection method for images and videos under uncontrolled conditions, based on a proposed Face Alignment Recurrent Network (FARN). The network works in recurrent fashion and is end-to-end trained to help avoid over-strong early stage regressors and over-weak later stage regressors as in many existing works. Long Short Term Memory (LSTM) model is employed in our network to make full use of the spatial and temporal middle stage information in a natural way, where by spatial we mean that for each image (frame), the predicted landmark position in the current stage will be used to guide the estimation for the next stage, and by temporal we mean that the predicted landmark position in the current frame will be used to guide the estimation for the next frame, and thus providing an unified framework for facial landmark detection in both images and videos. We conduct experiments on public image datasets (COFW, Helen, 300-W) as well as on video datasets (300-VW), and results show clear improvement over most of the current state-of-the-art approaches. In addition, it works in 18 ms per image (frame).(1) (C) 2017 Published by Elsevier Ltd.
Keyword:
Face alignment
Recurrent network
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations视觉基因组: 使用众包密集图像注释连接语言和视觉
OAAI
OAAI分享收藏Fast alignment for sparse representation based face recognition基于稀疏表示的快速对齐人脸识别.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Microstructure of Low-fat Cheddar Cheese Containing Varying Concentrations of Sucrose Polyesters
PREAI
分享收藏
查看更多内容