Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
11030076 | Image and Vision Computing | 2018 | 29 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Nate Crosswhite, Jeffrey Byrne, Chris Stauffer, Omkar Parkhi, Qiong Cao, Andrew Zisserman,