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Proceedings Paper

Power-law radon-transformed superimposed inverse filter synthetic discriminant correlator for facial recognition
Author(s): Bahareh Haji-saeed; Jed Khoury; Charles L. Woods; John Kierstead
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Paper Abstract

A power-law correlation based on an inverse filter Fourier-Radon-transform synthetic discriminant function (SDF) for facial recognition is proposed. In order to avoid spectral overlap and nonlinear crosstalk, superposition of rotationally variant sets of inverse filter Fourier-transformed Radon-processed templates is used to generate the SDF. For the inverse filter, the Fourier transform of M projections (Radon Transform) from one training image is combined with (N-1) M Fourier transform of M projections taken from another N-1 training image. This synthetic SDF filter has a very high discrimination capability; however, it is not noise robust. To overcome this problem, a power-law dynamic range compression is added to the correlation process. The proposed filter has three advantages: (1) high discrimination capability as an inverse filter, (2) noise robustness due to dynamic range compression, and (3) crosstalk-free nonlinear processing. The filter performance was evaluated by established metrics, such as peak-to-correlation energy (PCE), Horner efficiency, and correlation-peak intensity. The results showed significant improvement as the power-law filter compression increased.

Paper Details

Date Published: 17 March 2008
PDF: 7 pages
Proc. SPIE 6973, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2008, 69730M (17 March 2008); doi: 10.1117/12.777627
Show Author Affiliations
Bahareh Haji-saeed, Solid State Scientific Corp. (United States)
Jed Khoury, Air Force Research Lab./SNHC (United States)
Charles L. Woods, Air Force Research Lab./SNHC (United States)
John Kierstead, Solid State Scientific Corp. (United States)


Published in SPIE Proceedings Vol. 6973:
Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2008
William J. Tolone; William Ribarsky, Editor(s)

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