Bootstrapping Joint Bayesian model for robust face verification

doi:10.1109/ICB.2016.7550088

CORC > 重庆绿色智能技术研究院 > 中国科学院重庆绿色智能技术研究院 > 智能安全技术研究中心

	Bootstrapping Joint Bayesian model for robust face verification
	Cheng, Cheng 1; Xing, Junliang 2; Feng, Youji 1; Li, Deling 1; Zhou, Xiang-Dong1
	2016
会议日期	June 13, 2016 - June 16, 2016
会议地点	Halmstad, Sweden
DOI	10.1109/ICB.2016.7550088
英文摘要	Generative Bayesian models have exhibited good performance on the face verification problem, i.e., determining whether two faces are from the same person. As one of the most representative methods, the Joint Bayesian (JB) model represents two faces jointly by introducing some appropriate priors, providing better separability between different face classes. The EM-like learning algorithm of the JB model, however, are occasionally observed to have unsatisfactory converge property during the iterative training process. In this paper, we present a Bootstrapping Joint Bayesian (BJB) model which demonstrates good converging behavior. The BJB model explicitly addresses the classification difficulties of different classes by gradually re-weighting the training samples and driving the Bayesian models to pay more attentions to the hard training samples. Experiments on a new challenging benchmark demonstrate promising results of the proposed model, compared to the baseline Bayesian models. © 2016 IEEE.
会议录	9th IAPR International Conference on Biometrics, ICB 2016
语种	英语
内容类型	会议论文
源URL	[http://119.78.100.138/handle/2HOD01W0/4642]
专题	智能安全技术研究中心
作者单位	1.Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, China; 2.Institute of Automation, Chinese Academy of Sciences, China
推荐引用方式 GB/T 7714	Cheng, Cheng,Xing, Junliang,Feng, Youji,et al. Bootstrapping Joint Bayesian model for robust face verification[C]. 见:. Halmstad, Sweden. June 13, 2016 - June 16, 2016.