Knowledge Commons of Institute of Automation,CAS
Cascade learning from adversarial synthetic images for accurate pupil detection | |
Gou, Chao1,3![]() ![]() ![]() ![]() | |
发表期刊 | PATTERN RECOGNITION
![]() |
ISSN | 0031-3203 |
2019-04-01 | |
卷号 | 88期号:2020页码:584-594 |
摘要 | Image-based pupil detection, which aims to find the pupil location in an image, has been an active research topic in computer vision community. Learning-based approaches can achieve preferable results given large amounts of training data with eye center annotations. However, there are limited publicly available datasets with accurate eye center annotations and it is unreliable and time-consuming for manually labeling large amounts of training data. In this paper, inspired by learning from synthetic data in Parallel Vision framework, we introduce a step of parallel imaging built upon Generative Adversarial Networks (GANs) to generate adversarial synthetic images. In particular, we refine the synthetic eye images by the improved SimGAN using adversarial training scheme. For the computational experiments, we further propose a coarse-to-fine pupil detection framework based on shape augmented cascade regression models learning from the adversarial synthetic images. Experiments on benchmark databases of BioID, GI4E, and LFW show that the proposed work performs significantly better over other state-of-the-art methods by leveraging the power of cascade regression and adversarial image synthesis. (C) 2018 Elsevier Ltd. All rights reserved. |
关键词 | Cascade regression GANs Pupil detection |
DOI | 10.1016/j.patcog.2018.12.014 |
关键词[WOS] | EYE LOCALIZATION ; ROBUST |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Science Foundation[1145152] ; National Natural Science Foundation of China[61806198,61304200,61533019] ; National Natural Science Foundation of China[61806198,61304200,61533019] ; National Science Foundation[1145152] |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000457666900046 |
出版者 | ELSEVIER SCI LTD |
七大方向——子方向分类 | 人工智能+交通 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/25282 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Gou, Chao |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Rensselaer Polytech Inst, ISL, Troy, NY 12180 USA 3.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Gou, Chao,Zhang, Hui,Wang, Kunfeng,et al. Cascade learning from adversarial synthetic images for accurate pupil detection[J]. PATTERN RECOGNITION,2019,88(2020):584-594. |
APA | Gou, Chao,Zhang, Hui,Wang, Kunfeng,Wang, Fei-Yue,&Ji, Qiang.(2019).Cascade learning from adversarial synthetic images for accurate pupil detection.PATTERN RECOGNITION,88(2020),584-594. |
MLA | Gou, Chao,et al."Cascade learning from adversarial synthetic images for accurate pupil detection".PATTERN RECOGNITION 88.2020(2019):584-594. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Cascade learning fro(2057KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论