Large scale similarity learning using similar pairs for person verification

Yang Yang, Shengcai Liao, Zhen Lei, Stan Z. Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

67 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
PublisherAAAI press
Pages3655-3661
Number of pages7
ISBN (Electronic)9781577357605
Publication statusPublished - 2016
Externally publishedYes
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: Feb 12 2016Feb 17 2016

Publication series

Name30th AAAI Conference on Artificial Intelligence, AAAI 2016

Conference

Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/TerritoryUnited States
CityPhoenix
Period2/12/162/17/16

ASJC Scopus subject areas

  • Artificial Intelligence

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