Face recognition in unconstrained videos with matched background similarity

Lior Wolf, Tal Hassner, Itay Maoz

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء


Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this paper we make the following contributions. (a) We present a comprehensive database of labeled videos of faces in challenging, uncontrolled conditions (i.e., in the wild), the YouTube Faces database, along with benchmark, pair-matching tests 1 . (b) We employ our benchmark to survey and compare the performance of a large variety of existing video face recognition techniques. Finally, (c) we describe a novel set-to-set similarity measure, the Matched Background Similarity (MBGS). This similarity is shown to considerably improve performance on the benchmark tests.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيف2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
ناشرIEEE Computer Society
عدد الصفحات6
رقم المعيار الدولي للكتب (المطبوع)9781457703942
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 2011

سلسلة المنشورات

الاسمProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
رقم المعيار الدولي للدوريات (المطبوع)1063-6919


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