Learning-by-synthesis for Accurate Eye Detection
Gou C(苟超); Wu, Yue; Wang, Kang; Wang FY(王飞跃); Ji, Qiang
2016-12
会议名称International Conference on Pattern Recognition
会议录名称2016 23rd International Conference on Pattern Recognition
会议日期December 4-8, 2016
会议地点Cancún Center, Cancún, México
摘要
Cascade regression framework has been successfully applied to facial landmark detection and achieves state-of-the-art performance recently. It requires a large number of facial images with labeled landmarks for training regression models. We propose to use cascade regression framework to detect eye center by capturing its contextual and shape information of other related eye landmarks. While for eye detection, it is time-consuming to collect large scale training data and it also can be unreliable for accurate manual annotation of eye related landmarks. In addition, it is difficult to collect enough training
data to cover various illuminations, subjects with different head poses and gaze directions. To tackle this problem, we propose to learn cascade regression models from synthetic photorealistic data. In our proposed approach, eye region is coarsely localized by a facial landmark detection method first. Then we learn the cascade regression models iteratively to predict the eye shape updates based on local appearance and shape features. Experimental results on benchmark databases such as BioID and GI4E show that our proposed cascade regression models learned from synthetic data can accurately localize the eye center. Comparisons with existing methods also demonstrate our proposed framework can achieve preferable performance against state-of-the-art methods.
关键词Learning-by-synthesis Eye Detection Cascade Regression
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/14485
专题复杂系统管理与控制国家重点实验室_先进控制与自动化
作者单位1.Institute of Automation, Chinese Academy of Sciences
2.Rensselaer Polytechnic Institute
3.Qingdao Academy of Intelligent Industries
推荐引用方式
GB/T 7714
Gou C,Wu, Yue,Wang, Kang,et al. Learning-by-synthesis for Accurate Eye Detection[C],2016.
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