Active learning in face recognition: Using tracking to build a face model
Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt
This paper describes a method by which a computer can autonomously acquire training data for learning to recognize a user's face. The computer, in this method, actively seeks out opportunities to acquire informative face examples. Using the principles of co-training, it combines a face detector trained on a single input image with tracking to extract face examples for learning. Our results show that this method extracts well-localized, diverse face examples from video after being introduced to the user through only one input image. In addition to requiring very little human intervention, a second significant benefit to this method is that it doesn't rely on a statistical classifier trained on a pre-existing face database for face detection. Because it doesn't require pre-training, this method has built-in robustness for situations where the application conditions differ from the conditions under which training data were acquired.
Originalsprog | Engelsk |
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Tidsskrift | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
ISSN | 1063-6919 |
DOI | |
Status | Udgivet - 2006 |
Eksternt udgivet | Ja |
Begivenhed | 2006 Conference on Computer Vision and Pattern Recognition Workshops - New York, NY, USA Varighed: 17 jun. 2006 → 22 jun. 2006 |
Konference
Konference | 2006 Conference on Computer Vision and Pattern Recognition Workshops |
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Land | USA |
By | New York, NY |
Periode | 17/06/2006 → 22/06/2006 |
ID: 302054011