Active learning in face recognition: Using tracking to build a face model

Research output: Contribution to journal › Conference article › Research › peer-review

Standard

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

Harvard

Hewitt, R & Belongie, S 2006, 'Active learning in face recognition: Using tracking to build a face model', Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPRW.2006.23

APA

Hewitt, R., & Belongie, S. (2006). Active learning in face recognition: Using tracking to build a face model. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPRW.2006.23

Vancouver

Hewitt R, Belongie S. Active learning in face recognition: Using tracking to build a face model. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006. https://doi.org/10.1109/CVPRW.2006.23

Author

Hewitt, Robin ; Belongie, Serge. / Active learning in face recognition : Using tracking to build a face model. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006.

Bibtex

@inproceedings{630ef3436bbf46f5b2a19735a3f451a3,
title = "Active learning in face recognition: Using tracking to build a face model",
abstract = "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.",
author = "Robin Hewitt and Serge Belongie",
year = "2006",
doi = "10.1109/CVPRW.2006.23",
language = "English",
journal = "I E E E Conference on Computer Vision and Pattern Recognition. Proceedings",
issn = "1063-6919",
publisher = "Institute of Electrical and Electronics Engineers",
note = "2006 Conference on Computer Vision and Pattern Recognition Workshops ; Conference date: 17-06-2006 Through 22-06-2006",

}

RIS

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