Similarity scores based on background samples

Lior Wolf, Tal Hassner, Yaniv Taigman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Evaluating the similarity of images and their descriptors by employing discriminative learners has proven itself to be an effective face recognition paradigm. In this paper we show how "background samples", that is, examples which do not belong to any of the classes being learned, may provide a significant performance boost to such face recognition systems. In particular, we make the following contributions. First, we define and evaluate the "Two-Shot Similarity" (TSS) score as an extension to the recently proposed "One-Shot Similarity" (OSS) measure. Both these measures utilize background samples to facilitate better recognition rates. Second, we examine the ranking of images most similar to a query image and employ these as a descriptor for that image. Finally, we provide results underscoring the importance of proper face alignment in automatic face recognition systems. These contributions in concert allow us to obtain a success rate of 86.83% on the Labeled Faces in the Wild (LFW) benchmark, outperforming current state-of-the-art results.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
Number of pages10
EditionPART 2
StatePublished - 2010
Event9th Asian Conference on Computer Vision, ACCV 2009 - Xi'an, China
Duration: 23 Sep 200927 Sep 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5995 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th Asian Conference on Computer Vision, ACCV 2009


Dive into the research topics of 'Similarity scores based on background samples'. Together they form a unique fingerprint.

Cite this