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Model-based deconvolution of the human face

Maylin, M.I.S., Solomon, Christopher J., Gibson, Stuart J. (2005) Model-based deconvolution of the human face. In: Seventh IASTED International Conference on Signal and Image Processing, Honolulu, HI. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
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Abstract

Many practical deconvolution problems arise in which explicit knowledge of both the system PSF and the spectral characteristics of the noise are unknown. We describe and present an approach to deconvolution in this situation which is specifically matched to the forensically important problem of face identification. Our approach is to model both human faces and image aberrations in a statistical appearance framework using a representative sample of faces. Deconvolution is then achieved experimentally by moving along known transition curves in a parametric face space. Our preliminary studies demonstrate that the accuracy of the method is superior to maximum-likelihood blind deconvolution at low signal-noise ratios. A hybrid method in which the noisy face image is first projected into the model space and blind deconvolution then applied yields the best overall performance.

Item Type: Conference or workshop item (Paper)
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - Proc. Seventh IASTED Int. Conf. Sign. Imag. Proc. [Field not mapped to EPrints] AD - Forensic Imaging Group, School of Physical Sciences, University of Kent, United Kingdom [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] A4 - IASTED, Technical Committee on Signal Processing; IASTED, Technical Committee on Image Processing [Field not mapped to EPrints] C3 - Proceedings of the Seventh IASTED International Conference on Signal and Image Processing, SIP 2005 [Field not mapped to EPrints]
Uncontrolled keywords: Deconvolution, Face, Models, Restoration, Statistical, Texture, Face recognition, Image processing, Maximum likelihood estimation, Spurious signal noise, Statistical methods, Deconvolution, Face, Statistical, Texture, Signal processing
Subjects: Q Science
Divisions: Faculties > Sciences > School of Physical Sciences > Forensic Imaging Group
Depositing User: Giles Tarver
Date Deposited: 21 May 2015 10:05 UTC
Last Modified: 29 May 2019 14:34 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48543 (The current URI for this page, for reference purposes)
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