Active learning in face recognition: Using tracking to build a face model
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
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Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
ISSN | 1063-6919 |
DOIs | |
Publication status | Published - 2006 |
Externally published | Yes |
Event | 2006 Conference on Computer Vision and Pattern Recognition Workshops - New York, NY, United States Duration: 17 Jun 2006 → 22 Jun 2006 |
Conference
Conference | 2006 Conference on Computer Vision and Pattern Recognition Workshops |
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Country | United States |
City | New York, NY |
Period | 17/06/2006 → 22/06/2006 |
ID: 302054011