کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11030076 | 1646391 | 2018 | 29 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Template adaptation for face verification and identification
ترجمه فارسی عنوان
سازگاری الگو برای تایید و شناسایی چهره
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کلمات کلیدی
تشخیص چهره، بیومتریک، تأیید صحت،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Face recognition performance evaluation has traditionally focused on one-to-one verification, popularized by the Labeled Faces in the Wild data set [1] for imagery and the YouTubeFaces data set [2] for videos. In contrast, the newly released IJB-A face recognition data set [3] unifies evaluation of one-to-many face identification with one-to-one face verification over templates, or sets of imagery and videos for a subject. In this paper, we study the problem of template adaptation, a form of transfer learning to the set of media in a template. Extensive performance evaluations on IJB-A show a surprising result, that perhaps the simplest method of template adaptation, combining deep convolutional network features with template specific linear SVMs, outperforms the state-of-the-art by a wide margin. We study the effects of template size, negative set construction and classifier fusion on performance, then compare template adaptation to convolutional networks with metric learning, 2D and 3D alignment. Our unexpected conclusion is that these other methods, when combined with template adaptation, all achieve nearly the same top performance on IJB-A for template-based face verification and identification.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Image and Vision Computing - Volume 79, November 2018, Pages 35-48
Journal: Image and Vision Computing - Volume 79, November 2018, Pages 35-48
نویسندگان
Nate Crosswhite, Jeffrey Byrne, Chris Stauffer, Omkar Parkhi, Qiong Cao, Andrew Zisserman,