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
专题模式识别国家重点实验室_生物识别与安全技术研究
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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