Abstract
In this paper, we apply One-Class Classification methods in facial image analysis problems. We consider the cases where the available training data information originates from one class, or one of the available classes is of high importance. We propose a novel extension of the One-Class Extreme Learning Machines algorithm aiming at minimizing both the training error and the data dispersion and consider solutions that generate decision functions in the ELM space, as well as in ELM
spaces of arbitrary dimensionality. We evaluate the performance in publicly available datasets. The proposed method compares favourably to other state-of-the-art choices.
spaces of arbitrary dimensionality. We evaluate the performance in publicly available datasets. The proposed method compares favourably to other state-of-the-art choices.
Original language | English |
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Title of host publication | 2016 IEEE International Conference on Image Process (ICIP 2016) |
Subtitle of host publication | Proceedings of a meeting held 25-29 September 2016, Phoenix, AZ, USA |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1644-1648 |
Number of pages | 5 |
ISBN (Electronic) | 9781467399616 |
ISBN (Print) | 9781467399623 |
DOIs | |
Publication status | Published - Mar 2017 |
Event | IEEE International Conference on Image Processing - Phoenix, Arizona, United States Duration: 25 Sept 2016 → 28 Sept 2016 |
Publication series
Name | Proceedings of the IEEE International Conference on Image Processing (ICIP) |
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Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
ISSN (Print) | 2381-8549 |
Conference
Conference | IEEE International Conference on Image Processing |
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Abbreviated title | ICIP |
Country/Territory | United States |
City | Phoenix, Arizona |
Period | 25/09/16 → 28/09/16 |
Keywords
- Facial image analysis
- one-class classification
- regularization