Large Scale Similarity Learning Using Similar Pairs for Person Verification

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	Large Scale Similarity Learning Using Similar Pairs for Person Verification
	Yang Yang(杨阳); Shengcai Liao; Zhen Lei; Stan Z. Li; Yang Yang
	2016-05
会议名称	Association for the Advancement of Artificial Intelligence
会议录名称	AAAI
会议日期	2016, 02.12-02.17
会议地点	Phoenix, USA
摘要	In this paper, we propose a novel similarity measure and then introduce an efficient strategy to learn it by using only similar pairs for person verification. Unlike existing metric learning methods, we consider both the difference and commonness of an image pair to increase its discriminativeness. Under a pairconstrained Gaussian assumption, we show how to obtain the Gaussian priors (i.e., corresponding covariance matrices) of dissimilar pairs from those of similar pairs. The application of a log likelihood ratio makes the learning process simple and fast and thus scalable to large datasets. Additionally, our method is able to handle heterogeneous data well. Results on the challenging datasets of face verification (LFW and Pub- Fig) and person re-identification (VIPeR) show that our algorithm outperforms the state-of-the-art methods.
关键词	Person Re-identification Face Verification Large Scale Similarity Learning
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11850
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
通讯作者	Yang Yang
作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yang Yang,Shengcai Liao,Zhen Lei,et al. Large Scale Similarity Learning Using Similar Pairs for Person Verification[C],2016.

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