Multiple one-shots for utilizing class label information

Yaniv Taigman, Lior Wolf, Tal Hassner

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


The One-Shot Similarity measure has recently been introduced as a means of boosting the performance of face recognition systems. Given two vectors, their One-Shot Similarity score reflects the likelihood of each vector belonging to the same class as the other vector and not in a class defined by a fixed set of "negative™ examples. An appealing aspect of this approach is that it does not require class labeled training data. In this paper we explore how the One-Shot Similarity may nevertheless benefit from the availability of such labels. We make the following contributions: (a) we present a system utilizing subject and pose information to improve facial image pair-matching performance using multiple One-Shot scores; (b) we show how separating pose and identity may lead to better face recognition rates in unconstrained, "wild™ facial images; (c) we explore how far we can get using a single descriptor with different similarity tests as opposed to the popular multiple descriptor approaches; and (d) we demonstrate the benefit of learned metrics for improved One-Shot performance. We test the performance of our system on the challenging Labeled Faces in the Wild unrestricted benchmark and present results that exceed by a large margin results reported on the restricted benchmark.

שפה מקוריתאנגלית
כותר פרסום המארחBritish Machine Vision Conference, BMVC 2009 - Proceedings
מוציא לאורBritish Machine Vision Association, BMVA
מסת"ב (מודפס)1901725391, 9781901725391
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 2009
אירוע2009 20th British Machine Vision Conference, BMVC 2009 - London, בריטניה
משך הזמן: 7 ספט׳ 200910 ספט׳ 2009

סדרות פרסומים

שםBritish Machine Vision Conference, BMVC 2009 - Proceedings


כנס2009 20th British Machine Vision Conference, BMVC 2009

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