Active learning in face recognition: Using tracking to build a face model
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Active learning in face recognition : Using tracking to build a face model. / Hewitt, Robin; Belongie, Serge.
In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Active learning in face recognition
T2 - 2006 Conference on Computer Vision and Pattern Recognition Workshops
AU - Hewitt, Robin
AU - Belongie, Serge
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33845516433&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2006.23
DO - 10.1109/CVPRW.2006.23
M3 - Conference article
AN - SCOPUS:33845516433
JO - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings
JF - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings
SN - 1063-6919
Y2 - 17 June 2006 through 22 June 2006
ER -
ID: 302054011