Similarity scores based on background samples

Lior Wolf, Tal Hassner, Yaniv Taigman

פרסום מחקרי: פרק בספר / בדוח / בכנספרסום בספר כנסביקורת עמיתים


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.

שפה מקוריתאנגלית
כותר פרסום המארחComputer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
מספר עמודים10
מהדורהPART 2
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 2010
אירוע9th Asian Conference on Computer Vision, ACCV 2009 - Xi'an, סין
משך הזמן: 23 ספט׳ 200927 ספט׳ 2009

סדרות פרסומים

שםLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
מספרPART 2
כרך5995 LNCS
ISSN (מודפס)0302-9743
ISSN (אלקטרוני)1611-3349


כנס9th Asian Conference on Computer Vision, ACCV 2009

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